Methods and systems for classification to prognostic labels using expert inputs

ABSTRACT

A system for classification to prognostic labels using expert inputs includes a classification device. The classification device is designed and configured to record at least a physiological input pertaining to a human subject, receive at least an expert submission pertaining to the human subject, the at least an expert submission including at least a diagnostic constraint, and transmit at least a diagnostic output to a client device. The system includes a machine-learning module operating on the classification device, the machine-learning module designed and configured to receive training data relating physiological input data to diagnostic data and generate at least a diagnostic output using machine learning as a function of the training data, the at least an expert submission and the at least a physiological input.

FIELD OF THE INVENTION

The present invention generally relates to the field of machinelearning. In particular, the present invention is directed to methodsand systems for classification to prognostic labels using expert inputs.

BACKGROUND

Automated analysis of physiological data can be highly challenging dueto the multiplicity of types and sources of data to be analyzed, whichin turn is a reflection of the immense complexity of systems sorepresented. Burgeoning knowledge concerning microscopic and macroscopicphysiological states, and concomitantly expanding modes of detection andanalysis of the same, have further exacerbated this problem.

SUMMARY OF THE DISCLOSURE

In one aspect, a system for classification to prognostic labels usingexpert inputs includes a classification device. The classificationdevice is designed and configured to record at least a physiologicalinput pertaining to a human subject, receive at least an expertsubmission pertaining to the human subject, the at least an expertsubmission including at least a diagnostic constraint, and transmit atleast a diagnostic output to a client device. The system includes amachine-learning module operating on the classification device, themachine-learning module designed and configured to receive training datarelating physiological input data to diagnostic data and generate atleast a diagnostic output using machine learning as a function of thetraining data, the at least an expert submission and the at least aphysiological input.

In another aspect, a method of classification to prognostic labels usingexpert inputs includes recording, at a classification device, at least aphysiological input pertaining to a human subject. The method includesreceiving, at the classification device, at least an expert submissionpertaining to the human subject, the at least an expert submissionincluding at least a diagnostic constraint. The method includesreceiving, at the classification device, training data relatingphysiological input data to diagnostic data. The method includesgenerating, by the classification device, at least a diagnostic outputusing machine learning as a function of the at least an expertsubmission and the at least a physiological input. The method includestransmitting, by the classification device, at least a diagnostic outputto a client device.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for classification to prognostic labels using expert inputs;

FIG. 2 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 3 is a block diagram illustrating an exemplary embodiment of aphysiological sample database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of aprognostic label database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of anameliorative process label 144 database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of aprognostic label learner 148 and associated system elements;

FIG. 8 is a block diagram illustrating an exemplary embodiment of anameliorative process label learner 156 and associated system elements;

FIG. 9 illustrates flow diagram illustrating an exemplary embodiment ofa method of classification to prognostic labels; and

FIG. 10 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Embodiments of systems and methods disclosed herein may classifyphysiological samples to one or more prognostic labels using trainingsets correlating physiological state data to prognostic labels;prognostic labels are further linked to ameliorative process labels 144using additional training sets. Categorization of data elements intraining sets may be accomplished using unsupervised clusteringalgorithms; categorization may alternatively or additionally involveexpert data inputs provided by graphical user interface 108 entries orextracted using language processing algorithms from a corpus ofsubject-specific documents. Expert submissions are used to focus thegenerative process, which may be accomplished by pruning training data120, selection of particular machine-learning models, filtering output,or the like.

Referring now to the FIG. 1, an exemplary embodiment of a system 100classification to prognostic labels using expert inputs. System 100includes a classification device 104. Classification device 104 mayinclude any computing device as described below in reference to FIG. 10,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described belowin reference to FIG. 10. Classification device 104 may be housed with,may be incorporated in, or may incorporate one or more sensors of atleast a sensor. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. Classification device 104 may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. Classification device 104with one or more additional devices as described below in further detailvia a network interface device. Network interface device may be utilizedfor connecting a classification device 104 to one or more of a varietyof networks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. Classification device 104 mayinclude but is not limited to, for example, a classification device 104or cluster of computing devices in a first location and a secondcomputing device or cluster of computing devices in a second location.Classification device 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Classification device 104 may distribute one ormore computing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Classification device 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

Still referring to FIG. 1, classification device 104 and/or one or moremodules operating thereon may be designed and/or configured to performany method, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, classification device 104 may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Classification device104 may perform any step or sequence of steps as described in thisdisclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing. Any module ormodules introduced in this disclosure may be instantiated using anycombination of software and/or hardware commands or circuitry asdescribed in this disclosure, including without limitation logiccircuits, software programs using functions, methods, and/orobject-oriented programming, or the like. Although modules areintroduced conceptually in the ensuing disclosure as separate componentsfor the sake of clarity, persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware that a module may be created,as contemplated in the scope of this disclosure, by any combination ofcircuitry and/or software program commands stored in any form; forinstance, and without limitation, a module may not be identified withinsystem 100 and/or classification device 104 as a distinct entity orcomponent, but may exist only as the combination of elements and/orcommands performing the functions attributed herein to the module, andtwo or more modules may be partially or wholly combined together, mayshare functions, data, objects, and/or circuits.

With continued reference to FIG. 1, classification device 104 isdesigned and configured to record at least a physiological inputpertaining to a human subject. A “physiological input” as used in thisdisclosure, is an element of data describing information relevant tohuman subject's constitutional state, including without limitationsymptoms, conditions, prognoses, test results, concerns, reasons for avisit to a health professional, personal stories and/or informationconcerning the human subject's interests, relationships to other people,informal and/or formal personal or health support groups or persons, orthe like. At least a physiological input may include, withoutlimitation, at least a biological extraction. At least a biologicalextraction, as used herein, includes may include any element and/orelements of data suitable for use as an element of physiological statedata 128. Physiological state data may include any data indicative of aperson's physiological state; physiological state may be evaluated withregard to one or more measures of health of a person's body, one or moresystems within a person's body such as a circulatory system, a digestivesystem, a nervous system, or the like, one or more organs within aperson's body, and/or any other subdivision of a person's body usefulfor diagnostic or prognostic purposes. Physiological state data mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1, physiological state data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C(HbA1c)levels. Physiological state data may include, without limitation, one ormore measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data may include measuresof estimated glomerular filtration rate (eGFR). Physiological state datamay include quantities of C-reactive protein, estradiol, ferritin,folate, homocysteine, prostate-specific Ag, thyroid-stimulating hormone,vitamin D, 25 hydroxy, blood urea nitrogen, creatinine, sodium,potassium, chloride, carbon dioxide, uric acid, albumin, globulin,calcium, phosphorus, alkaline photophatase, alanine amino transferase,aspartate amino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data may includeantinuclear antibody levels. Physiological state data may includealuminum levels. Physiological state data may include arsenic levels.Physiological state data may include levels of fibronigen, plasmacystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1, physiological state data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Physiological statedata may include a measure of waist circumference. Physiological statedata may include body mass index (BMI). Physiological state data mayinclude one or more measures of bone mass and/or density such asdual-energy x-ray absorptiometry. Physiological state data may includeone or more measures of muscle mass. Physiological state data mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

Still viewing FIG. 1, physiological state data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state datamay include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain.

Continuing to refer to FIG. 1, physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processingmodules 112 as described in this disclosure.

Still referring to FIG. 1, physiological state data may include genomicdata, including deoxyribonucleic acid (DNA) samples and/or sequences,such as without limitation DNA sequences contained in one or morechromosomes in human cells. Genomic data may include, withoutlimitation, ribonucleic acid (RNA) samples and/or sequences, such assamples and/or sequences of messenger RNA (mRNA) or the like taken fromhuman cells. Genetic data may include telomere lengths. Genomic data mayinclude epigenetic data including data describing one or more states ofmethylation of genetic material. Physiological state data may includeproteomic data, which as used herein is data describing all proteinsproduced and/or modified by an organism, colony of organisms, or systemof organisms, and/or a subset thereof. Physiological state data mayinclude data concerning a microbiome of a person, which as used hereinincludes any data describing any microorganism and/or combination ofmicroorganisms living on or within a person, including withoutlimitation biomarkers, genomic data, proteomic data, and/or any othermetabolic or biochemical data useful for analysis of the effect of suchmicroorganisms on other physiological state data of a person, and/or onprognostic labels and/or ameliorative processes as described in furtherdetail below.

With continuing reference to FIG. 1, physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Physiological state data may includeany physiological state data, as described above, describing anymulticellular organism living in or on a person including any parasiticand/or symbiotic organisms living in or on the persons; non-limitingexamples may include mites, nematodes, flatworms, or the like. Examplesof physiological state data described in this disclosure are presentedfor illustrative purposes only and are not meant to be exhaustive.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples ofphysiological state data that may be used consistently with descriptionsof systems and methods as provided in this disclosure.

At least a biological extraction may include a physically extractedsample, where a “physically extracted sample” as used in this disclosureis a sample obtained by removing and analyzing tissue and/or fluid.Physically extracted sample may include without limitation a bloodsample, a tissue sample, a buccal swab, a mucous sample, a stool sample,a hair sample, a fingernail sample, or the like. Physically extractedsample may include, as a non-limiting example, at least a blood sample.As a further non-limiting example, at least a biological extraction mayinclude at least a genetic sample. At least a genetic sample may includea complete genome of a person or any portion thereof. At least a geneticsample may include a DNA sample and/or an RNA sample. At least abiological extraction may include an epigenetic sample, a proteomicsample, a tissue sample, a biopsy, and/or any other physically extractedsample. At least a biological extraction may include an endocrinalsample. As a further non-limiting example, the at least a biologicalextraction may include a signal from at least a sensor configured todetect physiological data of a user and recording the at least abiological extraction as a function of the signal. At least a sensor mayinclude any medical sensor and/or medical device configured to capturesensor data concerning a patient, including any scanning, radiologicaland/or imaging device such as without limitation x-ray equipment,computer assisted tomography (CAT) scan equipment, positron emissiontomography (PET) scan equipment, any form of magnetic resonance imagery(MRI) equipment, ultrasound equipment, optical scanning equipment suchas photo-plethysmographic equipment, or the like. At least a sensor mayinclude any electromagnetic sensor, including without limitationelectroencephalographic sensors, magnetoencephalographic sensors,electrocardiographic sensors, electromyographic sensors, or the like. Atleast a sensor may include a temperature sensor. At least a sensor mayinclude any sensor that may be included in a mobile device and/orwearable device, including without limitation a motion sensor such as aninertial measurement unit (IMU), one or more accelerometers, one or moregyroscopes, one or more magnetometers, or the like. At least a wearableand/or mobile device sensor may capture step, gait, and/or othermobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate or the like. At least a sensor may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, and/or blood pressure. At least a sensor may be apart of system 100 or may be a separate device in communication withsystem 100.

Still referring to FIG. 1, at least a first biological extraction mayinclude data describing one or more test results, including results ofmobility tests, stress tests, dexterity tests, endocrinal tests, genetictests, psychological tests and/or evaluations, electromyographic tests,biopsies, radiological tests, genetic tests, and/or sensory tests.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples of at least aphysiological sample consistent with this disclosure. At least aphysiological sample may be added to a database, such as a physiologicalsample database as described in further detail below.

With continued reference to FIG. 1, at least a physiological input mayinclude at least a prognostic label. A prognostic label, as describedherein, is an element of data identifying and/or describing a current,incipient, or probable future medical condition affecting a person;medical condition may include a particular disease, one or more symptomsassociated with a syndrome, a syndrome, and/or any other measure ofcurrent or future health and/or healthy aging. At least a prognosticlabel may be associated with a physical and/or somatic condition, amental condition such as a mental illness, neurosis, or the like, or anyother condition affecting human health that may be associated with oneor more elements of physiological state data as described in furtherdetail below. Conditions associated with prognostic labels may include,without limitation one or more diseases, defined for purposes herein asconditions that negatively affect structure and/or function of part orall of an organism. Conditions associated with prognostic labels mayinclude, without limitation, acute or chronic infections, includingwithout limitation infections by bacteria, archaea, viruses, viroids,prions, single-celled eukaryotic organisms such as amoeba, paramecia,trypanosomes, plasmodia, leishmania, and/or fungi, and/or multicellularparasites such as nematodes, arthropods, fungi, or the like. Prognosticlabels may be associated with one or more immune disorders, includingwithout limitation immunodeficiencies and/or auto-immune conditions.Prognostic labels may be associated with one or more metabolicdisorders. Prognostic labels may be associated with one or moreendocrinal disorders. Prognostic labels may be associated with one ormore cardiovascular disorders. Prognostic labels may be associated withone or more respiratory disorders. Prognostic labels may be associatedwith one or more disorders affecting connective tissue. Prognosticlabels may be associated with one or more digestive disorders.Prognostic labels may be associated with one or more neurologicaldisorders such as neuromuscular disorders, dementia, or the like.Prognostic labels may be associated with one or more disorders of theexcretory system, including without limitation nephrological disorders.Prognostic labels may be associated with one or more liver disorders.Prognostic labels may be associated with one or more disorders of thebones such as osteoporosis. Prognostic labels may be associated with oneor more disorders affecting joints, such as osteoarthritis, gout, and/orrheumatoid arthritis. Prognostic labels be associated with one or morecancers, including without limitation carcinomas, lymphomas, leukemias,germ cell tumor cancers, blastomas, and/or sarcomas. Prognostic labelsmay include descriptors of latent, dormant, and/or apparent disorders,diseases, and/or conditions. Prognostic labels may include descriptorsof conditions for which a person may have a higher than averageprobability of development, such as a condition for which a person mayhave a “risk factor”; for instance, a person currently suffering fromabdominal obesity may have a higher than average probability ofdeveloping type II diabetes. The above-described examples are presentedfor illustrative purposes only and are not intended to be exhaustive.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples of conditionsthat may be associated with prognostic labels as described in thisdisclosure.

Still referring to FIG. 1, at least a prognostic label may be stored inany suitable data and/or data type. For instance, and withoutlimitation, at least a prognostic label may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as at least a prognosticlabel may take; persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various forms which may be suitablefor use as at least a prognostic label consistently with thisdisclosure.

Continuing to refer to FIG. 1, at least a physiological input mayinclude at least an ameliorative process label 144. As used herein, anameliorative process label 144 is an identifier, which may include anyform of identifier suitable for use as a prognostic label as describedabove, identifying a process that tends to improve a physical conditionof a user, where a physical condition of a user may include, withoutlimitation, any physical condition identifiable using a prognosticlabel. Ameliorative processes may include, without limitation, exerciseprograms, including amount, intensity, and/or types of exerciserecommended. Ameliorative processes may include, without limitation,dietary or nutritional recommendations based on data includingnutritional content, digestibility, or the like. Ameliorative processesmay include one or more medical procedures. Ameliorative processes mayinclude one or more physical, psychological, or other therapies.Ameliorative processes may include one or more medications. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various processes that may be used as ameliorative processesconsistently with this disclosure.

Still referring to FIG. 1, at least a physiological input may includewithout limitation a reason for a human subject's current visit with ahealth professional. At least a physiological input may include currentstatus of following treatment plan with identified areas of focus. Atleast a physiological input may include personal story about a user suchas who they are, what they care about, and who supports them.

Continuing to refer to FIG. 1, classification device 104 is designed andconfigured to receive at least an expert submission pertaining to thehuman subject. At least an expert submission, as used herein, is asubmission provided by an expert in subjects pertaining toconstitutional state of human subject, including without limitationexpertise in a physiological state and/or status of human subject, inany test result and/or physiological state data as described above, inany subject described by a prognostic label as described above, in anysubject that may be described using an ameliorative process label 144 asdescribed above, or the like. At least an expert submission may bereceived, as a non-limiting example, from a medical professional such asa doctor, nurse, therapist, psychologist, medical technician, or thelike. At least an expert submission may be received via and/or from aclient device operated by an expert, such as a computer and/or computersystem operated in a medical professional's office, such as a doctor'soffice, a computing device incorporated in one or more medical devices,a mobile device operated by the expert, or the like.

Still referring to FIG. 1, at least an expert submission includes atleast a diagnostic constraint. As used in this disclosure, a “diagnosticconstraint” is an element of data restricting data, concerning a humansubject, to be used in or produced by a machine-learning module 116 asdescribed in further detail below to a smaller subset than the total setof all data concerning the human subject that could be used or producedby machine-learning module 116 as described in further detail below;restriction may include generating and/or selecting a smaller subset ofbiological extraction data concerning the human subject, generatingand/or selecting a smaller subset of training data 120, as described infurther detail below, to be used by machine-learning module 116,generating and/or selecting a smaller subset of machine-learning models,as described in further detail below, to be used by machine-learningmodule 116, generating and/or selecting a smaller subset ofmachine-learning processes, as described in further detail below, to beused by machine-learning module 116, generating and/or selecting asmaller subset of learners incorporated in machine-learning module 116,as described in further detail below, to be used by machine-learningmodule 116, and/or generating, filtering to, and/or selecting a smallersubset of machine-learning outputs, as described in further detailbelow, to be produced by machine-learning module 116. Diagnosticconstraint may include, for instance, a selection of what information adoctor or other expert needs system to generate such as diagnosticoutput, treatment plans, or the like. Diagnostic constraint may includea symptom, test result, particular biological extraction, and/orevaluation from a physical examination with human subject, a phone callwith human subject, or the like. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of diagnostic constraints as defined herein.

With continued reference to FIG. 1, classification device 104 mayreceive the at least an expert submission according to any suitableprocess. In an embodiment, classification device 104 and/or a userdevice connected to classification device 104 may provide a graphicaluser interface 108, which may include without limitation a form or othergraphical element having data entry fields, wherein an experts, mayenter at least a diagnostic constraint; fields in graphical userinterface 108 may provide options describing previously identifieddiagnostic constraints, which may include a comprehensive ornear-comprehensive list commonly selected, and/or previously entereddiagnostic constraints, for instance in “drop-down” lists, where anexpert may be able to select one or more entries to indicate selectionsof diagnostic constraints. Fields may include free-form entry fieldssuch as text-entry fields where an expert may be able to type orotherwise enter text, enabling expert to describe a diagnosticconstraint not listed in a drop-down list or the like. Graphical userinterface 108 or the like may include fields corresponding to prognosticlabels, ameliorative process labels 144, elements of physiological statedata, and/or biological extractions concerning, associated with, orextracted from human subject; for instance, an expert may be able toselect one or more such data elements of interest so as to identify datato which diagnostic constraint will constrain input and/or output data,and/or to identify data that expert wishes to eliminate from inputand/or output using at least a diagnostic constraint.

Still referring to FIG. 1, at least an expert submission may a textualsubmission such as without limitation text entered by an expert in atextual entry field, received from expert via electronic communication,or the like. Classification device 104 may include a language processingmodule 112 designed and configured to parse the at least a textualsubmission and extract the at least a diagnostic constraint. Languageprocessing module 112 may include any hardware and/or software module.Language processing module 112 may be configured to extract, from theone or more documents, one or more words. One or more words may include,without limitation, strings of one or characters, including withoutlimitation any sequence or sequences of letters, numbers, punctuation,diacritic marks, engineering symbols, geometric dimensioning andtolerancing (GD&T) symbols, chemical symbols and formulas, spaces,whitespace, and other symbols, including any symbols usable as textualdata as described above. Textual data may be parsed into tokens, whichmay include a simple word (sequence of letters separated by whitespace)or more generally a sequence of characters as described previously. Theterm “token,” as used herein, refers to any smaller, individualgroupings of text from a larger source of text; tokens may be broken upby word, pair of words, sentence, or other delimitation. These tokensmay in turn be parsed in various ways. Textual data may be parsed intowords or sequences of words, which may be considered words as well.Textual data may be parsed into “n-grams”, where all sequences of nconsecutive characters are considered. Any or all possible sequences oftokens or words may be stored as “chains”, for example for use as aMarkov chain or Hidden Markov Model.

Still referring to FIG. 1, language processing module 112 may compareextracted words to diagnostic constraints and/or subjects of diagnosticconstraints such as without limitation, data, categories of dataincluding categories of data as set forth in further detail below,categories of training data 120 such as without limitation dataidentifying cohorts including human subject, machine-learning modelssuch as without limitation machine-learning modules 116 relating tocohorts including human subject, prognostic labels of interest to theexpert, ameliorative labels of interest to the expert, biologicalextraction data of interest to the expert, and/or physiological statdata of interest to the expert, learners, categories of outputs, of thelike. In an embodiment, one or more diagnostic constraints and/orsubjects of diagnostic constraints may be enumerated, to find totalcount of mentions in textual data. Alternatively or additionally,language processing module 112 may operate to produce a languageprocessing model. Language processing model may include a programautomatically generated by classification device 104 and/or languageprocessing module 112 to produce associations between one or more wordsextracted from at least a document and detect associations, includingwithout limitation mathematical associations, between such words, and/orassociations of extracted words with diagnostic constraints and/orsubjects of diagnostic constraints. Associations between languageelements, where language elements include for purposes herein extractedwords, diagnostic constraints and/or subjects of diagnostic constraintsmay include, without limitation, mathematical associations, includingwithout limitation statistical correlations between any language elementand any other language element and/or language elements. Statisticalcorrelations and/or mathematical associations may include probabilisticformulas or relationships indicating, for instance, a likelihood that agiven extracted word indicates a given diagnostic constraint and/orsubject of diagnostic constraints. As a further example, statisticalcorrelations and/or mathematical associations may include probabilisticformulas or relationships indicating a positive and/or negativeassociation between at least an extracted word and/or a diagnosticconstraint and/or subject of diagnostic constraints; positive ornegative indication may include an indication that a given document isor is not indicating a diagnostic constraint and/or subjects ofdiagnostic constraint is or is not associated with a given word. Whethera phrase, sentence, word, or other textual element in a document orcorpus of documents constitutes a positive or negative indicator may bedetermined, in an embodiment, by mathematical associations betweendetected words, comparisons to phrases and/or words indicating positiveand/or negative indicators that are stored in memory at classificationdevice 104, or the like.

Still referring to FIG. 1, language processing module 112 and/orclassification device 104 may generate the language processing model byany suitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 112may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1, language processing module 112 may use acorpus of documents to generate associations between language elementsin a language processing module 112, and classification device 104 maythen use such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of an association between at least an extracted word and/ora diagnostic constraint and/or subject of diagnostic constraints. In anembodiment, classification device 104 may perform this analysis using aselected set of significant documents, such as documents identified byone or more experts as representing good science, good clinicalanalysis, or the like; an expert or experts may identify or enter suchdocuments via graphical user interface 108 as described in furtherdetail below, or may communicate identities of significant documentsaccording to any other suitable method of electronic communication, orby providing such identity to other persons who may enter suchidentifications into classification device 104. Documents may be enteredinto classification device 104 by being uploaded by an expert or otherpersons using, without limitation, file transfer protocol (FTP) or othersuitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, classification device 104 may automatically obtain thedocument using such an identifier, for instance by submitting a requestto a database or compendium of documents such as JSTOR as provided byIthaka Harbors, Inc. of New York.

Continuing to refer to FIG. 1, system 100 may include a machine-learningmodule 116 operating on the classification device 104. Machine-learningmodule 116 may include any suitable hardware or software module asdescribed herein or any combination thereof. Machine-learning module 116is designed and configured to receive training data 120. Training data120, as used herein, is data containing correlation that amachine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data 120 may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data 120 may evince one or more trendsin correlations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data 120 according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data 120 may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training data120 may include data entered in standardized forms by persons orprocesses, such that entry of a given data element in a given field in aform may be mapped to one or more descriptors of categories. Elements intraining data 120 may be linked to descriptors of categories by tags,tokens, or other data elements; for instance, and without limitation,training data 120 may be provided in fixed-length formats, formatslinking positions of data to categories such as comma-separated value(CSV) formats and/or self-describing formats such as extensible markuplanguage (XML), enabling processes or devices to detect categories ofdata.

Alternatively or additionally, and still referring to FIG. 1, trainingdata 120 may include one or more elements that are not categorized; thatis, training data 120 may not be formatted or contain descriptors forsome elements of data. Machine-learning algorithms and/or otherprocesses may sort training data 120 according to one or morecategorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name and/or a description of amedical condition or therapy may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data 120 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow.

With continued reference to FIG. 1, machine-learning module 116 isdesigned and configured to receive training data 120 relatingphysiological input data to diagnostic data. “Diagnostic data,” as usedherein, is data describing prognostic and/or ameliorative data relatingto a human subject; diagnostic data may include, without limitation,data usable for and/or labeled by prognostic labels and/or data usablefor and/or labeled by ameliorative process labels 144. Training data 120relating physiological input data to diagnostic data may include aplurality of entries, each entry including at least an element ofphysiological input data and at least a correlated element of diagnosticdata; for instance, and without limitation, an entry may include anelement of physiological state data 128 and a prognostic label, anelement of physiological state data 128 and an ameliorative processlabel 144, a prognostic label and an ameliorative process label 144,and/or a combination of an element of physiological state data 128, aprognostic label, and an ameliorative process label 144.

As a non-limiting, illustrative example, and still referring to FIG. 1,categorization device 104 may be configured to receive a first trainingset 124 including a plurality of first data entries, each first dataentry of the first training set 124 including at least an element ofphysiological state data 128 and at least a correlated first prognosticlabel 132. In an embodiment, an element of physiological data iscorrelated with a prognostic label where the element of physiologicaldata is located in the same data element and/or portion of data elementas the prognostic label; for example, and without limitation, an elementof physiological data is correlated with a prognostic element where bothelement of physiological data and prognostic element are containedwithin the same first data element of the first training set 124. As afurther example, an element of physiological data is correlated with aprognostic element where both share a category label as described infurther detail below, where each is within a certain distance of theother within an ordered collection of data in data element, or the like.Still further, an element of physiological data may be correlated with aprognostic label where the element of physiological data and theprognostic label share an origin, such as being data that was collectedwith regard to a single person or the like. In an embodiment, a firstdatum may be more closely correlated with a second datum in the samedata element than with a third datum contained in the same data element;for instance, the first element and the second element may be closer toeach other in an ordered set of data than either is to the thirdelement, the first element and second element may be contained in thesame subdivision and/or section of data while the third element is in adifferent subdivision and/or section of data, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various forms and/or degrees of correlation betweenphysiological data and prognostic labels that may exist in firsttraining set 124 and/or first data element consistently with thisdisclosure.

In an embodiment, and still referring to FIG. 1, classification device104 may be designed and configured to associate at least an element ofphysiological state data 128 with at least a category from a list ofsignificant categories of physiological state data. Significantcategories of physiological state data may include labels and/ordescriptors describing types of physiological state data that areidentified as being of high relevance in identifying prognostic labels.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data based on degree ofdiagnostic relevance to one or more impactful conditions and/or withinone or more medical or public health fields. For instance, and withoutlimitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly, measures of lipidlevels in blood, such as total cholesterol, HDL, triglycerides, LDL-Cand/or HDL-C may be recognized as useful in identifying conditions suchas poor thyroid function, insulin resistance, blood glucosedysregulation, magnesium deficiency, dehydration, kidney disease,familial hypercholesterolemia, liver dysfunction, oxidative stress,inflammation, malabsorption, anemia, alcohol abuse, diabetes,hypercholesterolemia, coronary artery disease, atherosclerosis, or thelike. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional categories ofphysiological data that may be used consistently with this disclosure.

Still referring to FIG. 1, classification device 104 may receive thelist of significant categories according to any suitable process; forinstance, and without limitation, classification device 104 may receivethe list of significant categories from at least an expert. In anembodiment, classification device 104 and/or a user device connected toclassification device 104 may provide a graphical user interface 108 asdescribed above, which may include without limitation a form or othergraphical element having data entry fields, wherein one or more experts,including without limitation clinical and/or scientific experts, mayenter information describing one or more categories of physiologicaldata that the experts consider to be significant or useful for detectionof conditions; fields in graphical user interface 108 may provideoptions describing previously identified categories, which may include acomprehensive or near-comprehensive list of types of physiological datadetectable using known or recorded testing methods, for instance in“drop-down” lists, where experts may be able to select one or moreentries to indicate their usefulness and/or significance in the opinionof the experts. Fields may include free-form entry fields such astext-entry fields where an expert may be able to type or otherwise entertext, enabling expert to propose or suggest categories not currentlyrecorded. Graphical user interface 108 or the like may include fieldscorresponding to prognostic labels, where experts may enter datadescribing prognostic labels and/or categories of prognostic labels theexperts consider related to entered categories of physiological data;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded prognosticlabels, and which may be comprehensive, permitting each expert to selecta prognostic label and/or a plurality of prognostic labels the expertbelieves to be predicted and/or associated with each category ofphysiological data selected by the expert. Fields for entry ofprognostic labels and/or categories of prognostic labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface 108 may provide an expert with a field in which toindicate a reference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with expert entries, or the like.

Referring again to FIG. 1, data information describing significantcategories of physiological data, relationships of such categories toprognostic labels, and/or significant categories of prognostic labelsmay alternatively or additionally be extracted from one or moredocuments using a language processing module 112 as described above.

Still referring to FIG. 1, language processing module 112 may compareextracted words to categories of physiological data recorded atclassification device 104, one or more prognostic labels recorded atclassification device 104, and/or one or more categories of prognosticlabels recorded at classification device 104; such data for comparisonmay be entered on classification device 104 as described above usingexpert data inputs or the like. In an embodiment, one or more categoriesmay be enumerated, to find total count of mentions in such documents.Alternatively or additionally, language processing module 112 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by classificationdevice 104 and/or language processing module 112 to produce associationsbetween one or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels. Associationsbetween language elements, where language elements include for purposesherein extracted words, categories of physiological data, relationshipsof such categories to prognostic labels, and/or categories of prognosticlabels may include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given extracted word indicates a givencategory of physiological data, a given relationship of such categoriesto prognostic labels, and/or a given category of prognostic labels. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels; positive or negative indication mayinclude an indication that a given document is or is not indicating acategory of physiological data, relationship of such category toprognostic labels, and/or category of prognostic labels is or is notsignificant. For instance, and without limitation, a negative indicationmay be determined from a phrase such as “telomere length was not foundto be an accurate predictor of overall longevity,” whereas a positiveindication may be determined from a phrase such as “telomere length wasfound to be an accurate predictor of dementia,” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat classification device 104, or the like.

Still referring to FIG. 1, language processing module 112 may use acorpus of documents, for instance as described above, to generateassociations between language elements in a language processing module112, and classification device 104 may then use such associations toanalyze words extracted from one or more documents and determine thatthe one or more documents indicate significance of a category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. In anembodiment, classification device 104 may perform this analysis using aselected set of significant documents, such as documents identified byone or more experts as representing good science, good clinicalanalysis, or the like; experts may identify or enter such documents viagraphical user interface 108 as described above in reference to FIG. 9,or may communicate identities of significant documents according to anyother suitable method of electronic communication, or by providing suchidentity to other persons who may enter such identifications intoclassification device 104. Documents may be entered into classificationdevice 104 by being uploaded by an expert or other persons using,without limitation, file transfer protocol (FTP) or other suitablemethods for transmission and/or upload of documents; alternatively oradditionally, where a document is identified by a citation, a uniformresource identifier (URI), uniform resource locator (URL) or other datumpermitting unambiguous identification of the document, classificationdevice 104 may automatically obtain the document using such anidentifier, for instance by submitting a request to a database orcompendium of documents such as JSTOR as provided by Ithaka Harbors,Inc. of New York.

Continuing to refer to FIG. 1, whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to prognostic labels, and/or a given category of prognosticlabels is entered via graphical user interface 108, alternativesubmission means, and/or extracted from a document or body of documentsas described above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to prognosticlabels, and/or category of prognostic labels may be given an overallsignificance score; overall significance score may, for instance, beincremented each time an expert submission and/or paper indicatessignificance as described above. Persons skilled in the art, uponreviewing the entirety of this disclosure will be aware of other ways inwhich scores may be generated using a plurality of entries, includingaveraging, weighted averaging, normalization, and the like. Significancescores may be ranked; that is, all categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels may be ranked according significance scores, forinstance by ranking categories of physiological data, relationships ofsuch categories to prognostic labels, and/or categories of prognosticlabels higher according to higher significance scores and loweraccording to lower significance scores. Categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels may be eliminated from current use ifthey fail a threshold comparison, which may include a comparison ofsignificance score to a threshold number, a requirement thatsignificance score belong to a given portion of ranking such as athreshold percentile, quartile, or number of top-ranked scores.Significance scores may be used to filter outputs as described infurther detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to prognostic labels,and/or category of prognostic labels is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to prognostic labels, and/or category of prognostic labelsis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to prognostic labels, and/or category of prognosticlabels is or is not significant per type of physiological sample, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 1, classification device 104 may detect furthersignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or categories of prognostic labelsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.

Continuing to refer to FIG. 1, in an embodiment, classification device104 may be configured, for instance as part of receiving the firsttraining set 124, to associate at least correlated first prognosticlabel 132 with at least a category from a list of significant categoriesof prognostic labels. Significant categories of prognostic labels may beacquired, determined, and/or ranked as described above. As anon-limiting example, prognostic labels may be organized according torelevance to and/or association with a list of significant conditions. Alist of significant conditions may include, without limitation,conditions having generally acknowledged impact on longevity and/orquality of life; this may be determined, as a non-limiting example, by aproduct of relative frequency of a condition within the population withyears of life and/or years of able-bodied existence lost, on average, asa result of the condition. A list of conditions may be modified for agiven person to reflect a family history of the person; for instance, aperson with a significant family history of a particular condition orset of conditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult classification device 104 may modify list of significantcategories to reflect this difference.

Still referring to FIG. 1, classification device 104 is designed andconfigured to receive a second training set 136 including a plurality ofsecond data entries. Each second data entry of the second training set136 includes at least a second prognostic label 140; at least a secondprognostic label 140 may include any label suitable for use as at leasta first prognostic label 132 as described above. Each second data entryof the second training set 136 includes at least an ameliorative processlabel 144, as described above, correlated with the at least a secondprognostic label 140, where correlation may include any correlationsuitable for correlation of at least a first prognostic label 132 to atleast an element of physiological data as described above.

Continuing to refer to FIG. 1, in an embodiment classification device104 may be configured, for instance as part of receiving second trainingset 136, to associate the at least second prognostic label 140 with atleast a category from a list of significant categories of prognosticlabels. This may be performed as described above for use of lists ofsignificant categories with regard to at least a first prognostic label132. Significance may be determined, and/or association with at least acategory, may be performed for prognostic labels in first training set124 according to a first process as described above and for prognosticlabels in second training set 136 according to a second process asdescribed above.

Still referring to FIG. 1, classification device 104 may be configured,for instance as part of receiving second training set 136, to associateat least a correlated ameliorative process label 144 with at least acategory from a list of significant categories of ameliorative processlabels 144. In an embodiment, classification device 104 and/or a userdevice connected to classification device 104 may provide a secondgraphical user interface 108 which may include without limitation a formor other graphical element having data entry fields, wherein one or moreexperts, including without limitation clinical and/or scientificexperts, may enter information describing one or more categories ofprognostic labels that the experts consider to be significant asdescribed above; fields in graphical user interface 108 may provideoptions describing previously identified categories, which may include acomprehensive or near-comprehensive list of types of prognostic labels,for instance in “drop-down” lists, where experts may be able to selectone or more entries to indicate their usefulness and/or significance inthe opinion of the experts. Fields may include free-form entry fieldssuch as text-entry fields where an expert may be able to type orotherwise enter text, enabling expert to propose or suggest categoriesnot currently recorded. Graphical user interface 108 or the like mayinclude fields corresponding to ameliorative labels, where experts mayenter data describing ameliorative labels and/or categories ofameliorative labels the experts consider related to entered categoriesof prognostic labels; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded ameliorative labels, and which may be comprehensive, permittingeach expert to select an ameliorative label and/or a plurality ofameliorative labels the expert believes to be predicted and/orassociated with each category of prognostic labels selected by theexpert. Fields for entry of ameliorative labels and/or categories ofameliorative labels may include free-form data entry fields such as textentry fields; as described above, examiners may enter data not presentedin pre-populated data fields in the free-form data entry fields.Alternatively or additionally, fields for entry of ameliorative labelsmay enable an expert to select and/or enter information describing orlinked to a category of ameliorative label that the expert considerssignificant, where significance may indicate likely impact on longevity,mortality, quality of life, or the like as described in further detailbelow. Graphical user interface 108 may provide an expert with a fieldin which to indicate a reference to a document describing significantcategories of prognostic labels, relationships of such categories toameliorative labels, and/or significant categories of ameliorativelabels. Such information may alternatively be entered according to anyother suitable means for entry of expert data as described above. Dataconcerning significant categories of prognostic labels, relationships ofsuch categories to ameliorative labels, and/or significant categories ofameliorative labels may be entered using analysis of documents usinglanguage processing module 112 or the like as described above.

In an embodiment, and still referring to FIG. 1, classification device104 may extract at least a second data entry from one or more documents;extraction may be performed using any language processing method asdescribed above. Classification device 104 may be configured, forinstance as part of receiving second training set 136, to receive atleast a document describing at least a medical history and extract atleast a second data entry of plurality of second data entries from theat least a document. A medical history document may include, forinstance, a document received from an expert and/or medical practitionerdescribing treatment of a patient; document may be anonymized by removalof one or more patient-identifying features from document. A medicalhistory document may include a case study, such as a case studypublished in a medical journal or written up by an expert. A medicalhistory document may contain data describing and/or described by aprognostic label; for instance, the medical history document may list adiagnosis that a medical practitioner made concerning the patient, afinding that the patient is at risk for a given condition and/or evincessome precursor state for the condition, or the like. A medical historydocument may contain data describing and/or described by an ameliorativeprocess label 144; for instance, the medical history document may list atherapy, recommendation, or other ameliorative process that a medicalpractitioner described or recommended to a patient. A medical historydocument may describe an outcome; for instance, medical history documentmay describe an improvement in a condition describing or described by aprognostic label, and/or may describe that the condition did notimprove. Prognostic labels, ameliorative process labels 144, and/orefficacy of ameliorative process labels 144 may be extracted from and/ordetermined from one or more medical history documents using anyprocesses for language processing as described above; for instance,language processing module 112 may perform such processes. As anon-limiting example, positive and/or negative indications regardingameliorative processes identified in medical history documents may bedetermined in a manner described above for determination of positiveand/or negative indications regarding categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels.

With continued reference to FIG. 1, classification device 104 may beconfigured, for instance as part of receiving second training set 136,to receiving at least a second data entry of the plurality of seconddata entries from at least an expert. This may be performed, withoutlimitation using second graphical user interface 108 as described above.Additional training sets may be received as part of training data 120;for instance, a third training set (not shown) may be received includinga plurality of third data entries, each data entry of the plurality ofthird data entries including at least a physiological state data elementand at least a correlated second ameliorative process label 144. Anydata entry of any of first, second, or third training sets may includeadditional correlated data; thus one or more data entries of first dataset may include correlated ameliorative process labels 144, one or moredata entries of second data set may include correlated physiologicalstate data elements, and/or one or more data entries of third data setmay include correlated prognostic labels.

Referring now to FIG. 2, data incorporated in at least a physiologicalinput, at least an expert submission, and/or in training data 120,including without limitation in first training set 124, second trainingset 136, and/or third training set, may be incorporated in one or moredatabases. As a non-limiting example, one or elements of physiologicalstate data may be stored in and/or retrieved from a physiological sampledatabase 200. A physiological sample database 200 may include any datastructure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. A physiological sampledatabase 200 may be implemented, without limitation, as a relationaldatabase, a key-value retrieval datastore such as a NOSQL database, orany other format or structure for use as a datastore that a personskilled in the art would recognize as suitable upon review of theentirety of this disclosure. A physiological sample database 200 mayinclude a plurality of data entries and/or records corresponding toelements of physiological data as described above. Data entries and/orrecords may describe, without limitation, data concerning particularphysiological samples that have been collected; entries may describereasons for collection of samples, such as without limitation one ormore conditions being tested for, which may be listed with relatedprognostic labels. Data entries may include prognostic labels and/orother descriptive entries describing results of evaluation of pastphysiological samples, including diagnoses that were associated withsuch samples, prognoses and/or conclusions regarding likelihood offuture diagnoses that were associated with such samples, and/or othermedical or diagnostic conclusions that were derived. Such conclusionsmay have been generated by system 100 in previous iterations of methods,with or without validation of correctness by medical professionals. Dataentries in a physiological sample database 200 may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database; one or moreadditional elements of information may include data associating aphysiological sample and/or a person from whom a physiological samplewas extracted or received with one or more cohorts, includingdemographic groupings such as ethnicity, sex, age, income, geographicalregion, or the like, one or more common diagnoses or physiologicalattributes shared with other persons having physiological samplesreflected in other data entries, or the like. Additional elements ofinformation may include one or more categories of physiological data asdescribed above. Additional elements of information may includedescriptions of particular methods used to obtain physiological samples,such as without limitation physical extraction of blood samples or thelike, capture of data with one or more sensors, and/or any otherinformation concerning provenance and/or history of data acquisition.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in aphysiological sample database 200 may reflect categories, cohorts,and/or populations of data consistently with this disclosure.

Referring now to FIG. 3, one or more database tables in physiologicalsample database 200 may include, as a non-limiting example, a prognosticlink table 300. Prognostic link table 300 may be a table relatingphysiological sample data as described above to prognostic labels; forinstance, where an expert has entered data relating a prognostic labelto a category of physiological sample data and/or to an element ofphysiological sample data via graphical user interface 108 as describedabove, one or more rows recording such an entry may be inserted inprognostic link table 300. Alternatively or additionally, linking ofprognostic labels to physiological sample data may be performed entirelyin a prognostic label database as described below.

With continued reference to FIG. 3, physiological sample database 200may include tables listing one or more samples according to samplesource. For instance, and without limitation, physiological sampledatabase 200 may include a fluid sample table 304 listing samplesacquired from a person by extraction of fluids, such as withoutlimitation blood, lymph cerebrospinal fluid, or the like. As anothernon-limiting example, physiological sample database 200 may include asensor data table 308, which may list samples acquired using one or moresensors, for instance as described in further detail below. As a furthernon-limiting example, physiological sample database 200 may include agenetic sample table 312, which may list partial or entire sequences ofgenetic material. Genetic material may be extracted and amplified, as anon-limiting example, using polymerase chain reactions (PCR) or thelike. As a further example, also non-limiting, physiological sampledatabase 200 may include a medical report table 316, which may listtextual descriptions of medical tests, including without limitationradiological tests or tests of strength and/or dexterity or the like.Data in medical report table may be sorted and/or categorized using alanguage processing module 112 312, for instance, translating a textualdescription into a numerical value and a label corresponding to acategory of physiological data; this may be performed using any languageprocessing algorithm or algorithms as referred to in this disclosure. Asanother non-limiting example, physiological sample database 200 mayinclude a tissue sample table 320, which may record physiologicalsamples obtained using tissue samples. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in physiologicalsample database 200 consistently with this disclosure.

Referring again to FIG. 2, classification device 104 and/or anotherdevice in system 100 may populate one or more fields in physiologicalsample database 200 using expert information, which may be extracted orretrieved from an expert knowledge database 204. An expert knowledgedatabase 204 may include any data structure and/or data store suitablefor use as a physiological sample database 200 as described above.Expert knowledge database 204 may include data entries reflecting one ormore expert submissions of data such as may have been submittedaccording to any process described above in reference to FIG. 1,including without limitation by using graphical user interface 108and/or second graphical user interface 108. Expert knowledge databasemay include one or more fields generated by language processing module112, such as without limitation fields extracted from one or moredocuments as described above. For instance, and without limitation, oneor more categories of physiological data and/or related prognosticlabels and/or categories of prognostic labels associated with an elementof physiological state data 128 as described above may be stored ingeneralized from in an expert knowledge database 204 and linked to,entered in, or associated with entries in a physiological sampledatabase 200. Documents may be stored and/or retrieved by classificationdevice 104 and/or language processing module 112 in and/or from adocument database 208; document database 208 may include any datastructure and/or data store suitable for use as physiological sampledatabase 200 as described above. Documents in document database 208 maybe linked to and/or retrieved using document identifiers such as URIand/or URL data, citation data, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which documents may be indexed and retrieved according tocitation, subject matter, author, date, or the like as consistent withthis disclosure.

Referring now to FIG. 4, an exemplary embodiment of an expert knowledgedatabase 204 is illustrated. Expert knowledge database 204 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 204 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 200 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 4, one or more database tables in expertknowledge database 204 may include, as a non-limiting example, an expertprognostic table 400. Expert prognostic table 400 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via graphical user interface 108 asdescribed above, one or more rows recording such an entry may beinserted in expert prognostic table 400. In an embodiment, a formsprocessing module 404 may sort data entered in a submission viagraphical user interface 108 by, for instance, sorting data from entriesin the graphical user interface 108 to related categories of data; forinstance, data entered in an entry relating in the graphical userinterface 108 to a prognostic label may be sorted into variables and/ordata structures for storage of prognostic labels, while data entered inan entry relating to a category of physiological data and/or an elementthereof may be sorted into variables and/or data structures for thestorage of, respectively, categories of physiological data or elementsof physiological data. Where data is chosen by an expert frompre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, language processingmodule 112 may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map physiological data to an existing label.Alternatively or additionally, when a language processing algorithm,such as vector similarity comparison, indicates that an entry is not asynonym of an existing label, language processing module 112 mayindicate that entry should be treated as relating to a new label; thismay be determined by, e.g., comparison to a threshold number of cosinesimilarity and/or other geometric measures of vector similarity of theentered text to a nearest existent label, and determination that adegree of similarity falls below the threshold number and/or a degree ofdissimilarity falls above the threshold number. Data from expert textualsubmissions 408, such as accomplished by filling out a paper or PDF formand/or submitting narrative information, may likewise be processed usinglanguage processing module 112. Data may be extracted from expert papers412, which may include without limitation publications in medical and/orscientific journals, by language processing module 112 via any suitableprocess as described herein. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalmethods whereby novel terms may be separated from already-classifiedterms and/or synonyms therefore, as consistent with this disclosure.Expert prognostic table 400 may include a single table and/or aplurality of tables; plurality of tables may include tables forparticular categories of prognostic labels such as a current diagnosistable, a future prognosis table, a genetic tendency table, a metabolictendency table, and/or an endocrinal tendency table (not shown), to namea few non-limiting examples presented for illustrative purposes only.

With continued reference to FIG. 4, one or more database tables inexpert knowledge database 204 may include, as a further non-limitingexample tables listing one or more ameliorative process labels 144;expert data populating such tables may be provided, without limitation,using any process described above, including entry of data from secondgraphical user interface 108 via forms processing module 404 and/orlanguage processing module 112, processing of textual submissions 408,or processing of expert papers 412. For instance, and withoutlimitation, an ameliorative nutrition table 416 may list one or moreameliorative processes based on nutritional instructions, and/or linksof such one or more ameliorative processes to prognostic labels, asprovided by experts according to any method of processing and/orentering expert data as described above. As a further example anameliorative action table 420 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above. As an additional example, an ameliorativesupplement table 424 may list one or more ameliorative processes basedon nutritional supplements, such as vitamin pills or the like, and/orlinks of such one or more ameliorative processes to prognostic labels,as provided by experts according to any method of processing and/orentering expert data as described above. As a further non-limitingexample, an ameliorative medication table 428 may list one or moreameliorative processes based on medications, including withoutlimitation over-the-counter and prescription pharmaceutical drugs,and/or links of such one or more ameliorative processes to prognosticlabels, as provided by experts according to any method of processingand/or entering expert data as described above. As an additionalexample, a counterindication table 432 may list one or morecounter-indications for one or more ameliorative processes;counterindications may include, without limitation allergies to one ormore foods, medications, and/or supplements, side-effects of one or moremedications and/or supplements, interactions between medications, foods,and/or supplements, exercises that should not be used given one or moremedical conditions, injuries, disabilities, and/or demographiccategories, or the like. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database204 consistently with this disclosure.

Referring again to FIG. 2, a prognostic label database 212, which may beimplemented in any manner suitable for implementation of physiologicalsample database 200, may be used to store prognostic labels used insystem 100, including any prognostic labels correlated with elements ofphysiological data in first training set 124 as described above;prognostic labels may be linked to or refer to entries in physiologicalsample database 200 to which prognostic labels correspond. Linking maybe performed by reference to historical data concerning physiologicalsamples, such as diagnoses, prognoses, and/or other medical conclusionsderived from physiological samples in the past; alternatively oradditionally, a relationship between a prognostic label and a data entryin physiological sample database 200 may be determined by reference to arecord in an expert knowledge database 204 linking a given prognosticlabel to a given category of physiological sample as described above.Entries in prognostic label database 212 may be associated with one ormore categories of prognostic labels as described above, for instanceusing data stored in and/or extracted from an expert knowledge database204.

Referring now to FIG. 5, an exemplary embodiment of a prognostic labeldatabase 212 is illustrated. Prognostic label database 212 may, as anon-limiting example, organize data stored in the prognostic labeldatabase 212 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofprognostic label database 212 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 5, one or more database tables in prognosticlabel database 212 may include, as a non-limiting example, a sample datatable 500. Sample data table 500 may be a table listing sample data,along with, for instance, one or more linking columns to link such datato other information stored in prognostic label database 212. In anembodiment, sample data 504 may be acquired, for instance fromphysiological sample database 200, in a raw or unsorted form, and may betranslated into standard forms, such as standard units of measurement,labels associated with particular physiological data values, or thelike; this may be accomplished using a data standardization module 508,which may perform unit conversions or the like. Data standardizationmodule 508 may alternatively or additionally map textual information,such as labels describing values tested for or the like, using languageprocessing module 112 or equivalent components and/or algorithmsthereto.

Continuing to refer to FIG. 5, prognostic label database 212 may includea sample label table 512; sample label table 512 may list prognosticlabels received with and/or extracted from physiological samples, forinstance as received in the form of sample text 516. A languageprocessing module 112 may compare textual information so received toprognostic labels and/or form new prognostic labels according to anysuitable process as described above. A sample prognostic link table 520may combine samples with prognostic labels, as acquired from samplelabel table 512 and/or expert knowledge database 204; combination may beperformed by listing together in rows or by relating indices or commoncolumns of two or more tables to each other. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in expertknowledge database 204 consistently with this disclosure.

Referring again to FIG. 2, first training set 124 may be populated byretrieval of one or more records from physiological sample database 200and/or prognostic label database 212; in an embodiment, entriesretrieved from physiological sample database 200 and/or prognostic labeldatabase 212 may be filtered and or select via query to match one ormore additional elements of information as described above, so as toretrieve a first training set 124 including data belonging to a givencohort, demographic population, or other set, so as to generate outputsas described below that are tailored to a person or persons with regardto whom system 100 classifies physiological samples to prognostic labelsas set forth in further detail below. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which records may be retrieved from physiological sample database 200and/or prognostic label database to generate a first training set 124 toreflect individualized group data pertaining to a person of interest inoperation of system and/or method, including without limitation a personwith regard to whom at least a physiological sample is being evaluatedas described in further detail below. Classification device 104 mayalternatively or additionally receive a first training set 124 and storeone or more entries in physiological sample database 200 and/orprognostic label database 212 as extracted from elements of firsttraining set 124.

Still referring to FIG. 2, system 100 may include or communicate with anameliorative process label 144 database 216; an ameliorative processlabel 144 database 216 may include any data structure and/or datastoresuitable for use as a physiological sample database 200 as describedabove. An ameliorative process label 144 database 216 may include one ormore entries listing labels associated with one or more ameliorativeprocesses as described above, including any ameliorative labelscorrelated with prognostic labels in second training set 136 asdescribed above; ameliorative process labels 144 may be linked to orrefer to entries in prognostic label database 212 to which ameliorativeprocess labels 144 correspond. Linking may be performed by reference tohistorical data concerning prognostic labels, such as therapies,treatments, and/or lifestyle or dietary choices chosen to alleviateconditions associated with prognostic labels in the past; alternativelyor additionally, a relationship between an ameliorative process label144 and a data entry in prognostic label database 212 may be determinedby reference to a record in an expert knowledge database 204 linking agiven ameliorative process label 144 to a given category of prognosticlabel as described above. Entries in ameliorative process label 144database 212 may be associated with one or more categories of prognosticlabels as described above, for instance using data stored in and/orextracted from an expert knowledge database 204.

Referring now to FIG. 6, an exemplary embodiment of an ameliorativeprocess label 144 database 216 is illustrated. Ameliorative processlabel 144 database 216 may, as a non-limiting example, organize datastored in the ameliorative process label 144 database 216 according toone or more database tables. One or more database tables may be linkedto one another by, for instance, common column values. For instance, acommon column between two tables of ameliorative process label 144database 216 may include an identifier of an expert submission, such asa form entry, textual submission, expert paper, or the like, forinstance as defined below; as a result, a query may be able to retrieveall rows from any table pertaining to a given submission or set thereof.Other columns may include any other category usable for organization orsubdivision of expert data, including types of expert data, names and/oridentifiers of experts submitting the data, times of submission, or thelike; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which expert data from oneor more tables may be linked and/or related to expert data in one ormore other tables.

Still referring to FIG. 6, ameliorative process label 144 database 216may include a prognostic link table 600; prognostic link table may linkameliorative process data to prognostic label data, using any suitablemethod for linking data in two or more tables as described above.Ameliorative process label 144 database 216 may include an ameliorativenutrition table 604, which may list one or more ameliorative processesbased on nutritional instructions, and/or links of such one or moreameliorative processes to prognostic labels, for instance as provided byexperts according to any method of processing and/or entering expertdata as described above, and/or using one or more machine-learningprocesses as set forth in further detail below. As a further example anameliorative action table 608 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above and/or using one or more machine-learningprocesses as set forth in further detail below. As an additionalexample, an ameliorative supplement table 612 may list one or moreameliorative processes based on nutritional supplements, such as vitaminpills or the like, and/or links of such one or more ameliorativeprocesses to prognostic labels, as provided by experts according to anymethod of processing and/or entering expert data as described aboveand/or using one or more machine-learning processes as set forth infurther detail below. As a further non-limiting example, an ameliorativemedication table 616 may list one or more ameliorative processes basedon medications, including without limitation over-the-counter andprescription pharmaceutical drugs, and/or links of such one or moreameliorative processes to prognostic labels, as provided by expertsaccording to any method of processing and/or entering expert data asdescribed above and/or using one or more machine-learning processes asset forth in further detail below. As an additional example, acounterindication table 620 may list one or more counter-indications forone or more ameliorative processes; counterindications may include,without limitation allergies to one or more foods, medications, and/orsupplements, side-effects of one or more medications and/or supplements,interactions between medications, foods, and/or supplements, exercisesthat should not be used given one or more medical conditions, injuries,disabilities, and/or demographic categories, or the like; this may beacquired using expert submission as described above and/or using one ormore machine-learning processes as set forth in further detail below.Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in ameliorative process database 216 consistently withthis disclosure.

Referring again to FIG. 2, second training set 136 may be populated byretrieval of one or more records from prognostic label database 212and/or ameliorative process label 144 database 216; in an embodiment,entries retrieved from prognostic label database 212 and/or ameliorativeprocess label 144 database 216 may be filtered and or select via queryto match one or more additional elements of information as describedabove, so as to retrieve a second training set 136 including databelonging to a given cohort, demographic population, or other set, so asto generate outputs as described below that are tailored to a person orpersons with regard to whom system 100 classifies prognostic labels toameliorative process labels 144 as set forth in further detail below.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which records may beretrieved from prognostic label database 212 and/or ameliorative processlabel 144 database 216 to generate a second training set 136 to reflectindividualized group data pertaining to a person of interest inoperation of system and/or method, including without limitation a personwith regard to whom at least a physiological sample is being evaluatedas described in further detail below. Classification device 104 mayalternatively or additionally receive a second training set 136 andstore one or more entries in prognostic label database 212 and/orameliorative process label 144 database 216 as extracted from elementsof second training set 136.

In an embodiment, and still referring to FIG. 2, classification device104 may receive an update to one or more elements of data represented infirst training set 124 and/or second training set 136, and may performone or more modifications to first training set 124 and/or secondtraining set 136, or to physiological sample database 200, expertknowledge database 204, prognostic label database 212, and/orameliorative process label 144 database 216 as a result. For instance aphysiological sample may turn out to have been erroneously recorded;classification device 104 may remove it from first training set 124,second training set 136, physiological sample database 200, expertknowledge database 204, prognostic label database 212, and/orameliorative process label 144 database 216 as a result. As a furtherexample, a medical and/or academic paper, or a study on which it wasbased, may be revoked; classification device 104 may remove it fromfirst training set 124, second training set 136, physiological sampledatabase 200, expert knowledge database 204, prognostic label database212, and/or ameliorative process label 144 database 216 as a result.Information provided by an expert may likewise be removed if the expertloses credentials or is revealed to have acted fraudulently.

Continuing to refer to FIG. 2, elements of data of first training set124, second training set 136, physiological sample database 200, expertknowledge database 204, prognostic label database 212, and/orameliorative process label 144 database 216 may have temporalattributes, such as timestamps; classification device 104 may order suchelements according to recency, select only elements more recentlyentered for first training set 124 and/or second training set 136, orotherwise bias training sets, database entries, and/or machine-learningmodels as described in further detail below toward more recent or lessrecent entries. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various ways in which temporalattributes of data entries may be used to affect results of methodsand/or systems as described herein.

Referring again to FIG. 1, machine-learning module 116 is configured togenerate at least a diagnostic output using machine learning as afunction of the at least an expert submission and the at least aphysiological input. As used herein, a “diagnostic output” is an outputcontaining at least a prognostic label and/or at least an ameliorativelabel, where the at least a prognostic label and/or at least anameliorative label is output by machine-learning module 116 as afunction of at least a physiological input. For example, and withoutlimitation, a diagnostic output may include a prognostic labelassociated with at least a physiological input, such as a diagnosis of acondition, syndrome, or likely future condition or syndrome based ondata collected as at least a biological sample or as a user query. Asanother non-limiting example, a diagnostic output may include anameliorative process label 144 associated with a prognostic label and/orother datum of at least a physiological input; for instance, adiagnostic output may include a treatment for a symptom and/or conditiondescribed by a user and/or revealed in or as a result of a biologicalextraction. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various additional examples for adiagnostic output as defined and described herein.

Still referring to FIG. 1, machine-learning module 116 may be designedand configured to generate at least a prognostic output by creating atleast a machine-learning model relating data useable for at least aphysiological input to data usable for at least a diagnostic outputusing training data 120 and generating the at least a diagnostic outputusing the at least a machine-learning model; at least a machine-learningmodel may include one or more models that determine a mathematicalrelationship between physiological state data and prognostic labels.Such models may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.Machine-learning may include other regression algorithms, includingwithout limitation polynomial regression.

Continuing to refer to FIG. 1, machine-learning algorithm used togenerate at least a machine-learning model may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naive Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 1, machine-learning module 116 may generateprognostic output using alternative or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdata set are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using first trainingset 124; the trained network may then be used to apply detectedrelationships between physiological input data and diagnostic outputdata.

Continuing to refer to FIG. 1, machine-learning module 116 may generateat least a machine-learning model using one or more supervised machinelearning algorithms. Supervised machine learning algorithms, as definedherein, include algorithms that receive a training set relating a numberof inputs to a number of outputs, and seek to find one or moremathematical relations relating inputs to outputs, where each of the oneor more mathematical relations is optimal according to some criterionspecified to the algorithm using some scoring function. For instance, asupervised learning algorithm may use elements of physiological inputdata as inputs, elements of data usable for diagnostic outputs asoutputs, and a scoring function representing a desired form ofrelationship to be detected between inputs and outputs; scoring functionmay, for instance, seek to maximize the probability that a input and/orcombination of inputs is associated with a given outputs or combinationof outputs, to minimize the probability that a given input and/orcombination of inputs is not associated with a given output and/orcombination of outputs. Scoring function may be expressed as a riskfunction representing an “expected loss” of an algorithm relating inputsto outputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in first training set124. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of supervisedmachine learning algorithms that may be used to determine relationbetween inputs and outputs. In an embodiment, one or more supervisedmachine-learning algorithms may be restricted to a particular domain forinstance, a supervised machine-learning process may be performed withrespect to a given set of parameters and/or categories of parametersthat have been suspected to be related to a given set of diagnosticoutput data, and/or are specified as linked to a medical specialtyand/or field of medicine covering a particular set of diagnostic outputdata. As a non-limiting example, a particular set of blood testbiomarkers and/or sensor data may be typically used by cardiologists todiagnose or predict various cardiovascular conditions, and a supervisedmachine-learning process may be performed to relate those blood testbiomarkers and/or sensor data to the various cardiovascular conditions;in an embodiment, domain restrictions of supervised machine-learningprocedures may improve accuracy of resulting models by ignoringartifacts in training data 120. Domain restrictions may be suggested byexperts and/or deduced from known purposes for particular evaluationsand/or known tests used to evaluate input data. Additional supervisedlearning processes may be performed without domain restrictions todetect, for instance, previously unknown and/or unsuspectedrelationships between input data and output data.

Still referring to FIG. 1, machine-learning module 116 may generate atleast a machine-learning model using one or more unsupervised processes.An unsupervised machine-learning process, as used herein, is a processthat derives inferences in datasets without regard to labels; as aresult, an unsupervised machine-learning process may be free to discoverany structure, relationship, and/or correlation provided in the data.For instance, and without limitation, machine-learning module 116 and/orclassification device 104 may perform an unsupervised machine learningprocess on training data 120, which may cluster data of training data120 according to detected relationships between elements of trainingdata 120, including without limitation correlations of elements ofphysiological state data to each other, correlations of prognosticlabels to each other, and/or ameliorative process labels 144 to eachother; such relations may then be combined with supervised machinelearning results to add new criteria for machine-learning module 116 toapply in relating inputs to outputs.

Still referring to FIG. 1, classification device 104 and/ormachine-learning module 116 may detect further significant categories ofphysiological input data and/or relationships of such categories todiagnostic output data, using machine-learning processes, includingwithout limitation unsupervised machine-learning processes as describedabove; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above. In anembodiment, as additional data is added to system 100, machine-learningmodule 116 and/or classification device 104 may continuously oriteratively perform unsupervised machine-learning processes to detectrelationships between different elements of the added and/or overalldata; in an embodiment, this may enable system 100 to use detectedrelationships to discover new correlations between known biomarkers,prognostic labels, and/or ameliorative labels and one or more elementsof data in large bodies of data, such as genomic, proteomic, and/ormicrobiome-related data, enabling future supervised learning and/or lazylearning processes as described in further detail below to identifyrelationships between, e.g., particular clusters of genetic alleles andparticular prognostic labels and/or suitable ameliorative labels. Use ofunsupervised learning may greatly enhance the accuracy and detail withwhich system may detect prognostic labels and/or ameliorative labels.

With continued reference to FIG. 1, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of ameliorative label; as illustrative examples, cohort couldinclude all people having a certain level or range of levels of bloodtriglycerides, all people diagnosed with type II diabetes, all peoplewho regularly run between 10 and 15 miles per week, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of a multiplicity of ways in which cohorts and/or other sets ofdata may be defined and/or limited for a particular unsupervisedlearning process.

Still referring to FIG. 1, machine-learning module 116 may alternativelyor additionally be designed and configured to generate at least adiagnostic output by executing a lazy learning process as a function ofthe first training set 124 and the at least a biological extraction. Alazy-learning process and/or protocol, which may alternatively bereferred to as a “lazy loading” or “call-when-needed” process and/orprotocol, may be a process whereby machine learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover a “first guess” at a diagnostic output associatedwith at least a physiological input, using training data 120. As anon-limiting example, an initial heuristic may include a ranking ofdiagnostic output data according to relation to a test type of at leasta biological extraction, one or more categories of physiological dataidentified in test type of at least a biological extraction, one or morevalues detected in at least a biological extraction, and/or one or moreprognostic or ameliorative process labels 144; ranking may include,without limitation, ranking according to significance scores ofassociations between elements of physiological input data and diagnosticoutput data, for instance as calculated as described above. Heuristicmay include selecting some number of highest-ranking associations and/ordiagnostic output data. Machine-learning module 116 may alternatively oradditionally implement any suitable “lazy learning” algorithm, includingwithout limitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate prognostic outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

In an embodiment, and continuing to refer to FIG. 1, machine-learningmodule 116 may generate a plurality of diagnostic outputs havingdifferent implications for a particular human subject. In such asituation, machine-learning module 116 and/or classification device 104may perform additional processes to filter diagnostic outputs. Filteringdiagnostic outputs may include filtering according to at least an expertsubmission; in an embodiment this may include presenting multiplepossible results to a medical practitioner, informing the medicalpractitioner that one or more follow-up tests and/or physiologicalsamples are needed to further determine a more definite prognosticlabel. Alternatively or additionally, expert submission may be receivedprior to generation of plurality of diagnostic outputs. Processes mayalternatively or additionally include additional machine learning steps;for instance, where reference to a model generated using supervisedlearning on a limited domain has produced multiple mutually exclusiveresults and/or multiple results that are unlikely all to be correct, ormultiple different supervised machine learning models in differentdomains may have identified mutually exclusive results and/or multipleresults that are unlikely all to be correct. In such a situation,machine-learning module 116 and/or classification device 104 may operatea further algorithm to determine which of the multiple outputs is mostlikely to be correct; algorithm may include use of an additionalsupervised and/or unsupervised model. Alternatively or additionally,machine-learning module 116 may perform one or more lazy learningprocesses using a more comprehensive set of user data to identify a moreprobably correct result of the multiple results. Results may bepresented and/or retained with rankings, for instance to advise amedical professional of the relative probabilities of various diagnosticoutputs being correct; alternatively or additionally, diagnostic outputsassociated with a probability of correctness below a given thresholdand/or diagnostic outputs contradicting results of the additionalprocess, may be eliminated. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in whichadditional processing may be used to determine relative likelihoods ofdiagnostic outputs on a list of multiple diagnostic outputs, and/or toeliminate some labels from such a list.

Still referring to FIG. 1, machine-learning module 116 is configured togenerate at least a diagnostic output as a function of at least anexpert submission. For instance, and without limitation, machinelearning module may be configured to generate a plurality of diagnosticoutputs and filter the plurality of diagnostic outputs using the atleast a diagnostic constraint, for instance as described above.Machine-learning module 116 may alternatively or additionally beconfigured to filter training data 120 according to the at least adiagnostic constraint and/or expert submission. For instance, an expertmay enter a submission limiting training data 120 to a cohort includinghuman subject, which may be any cohort as described in this disclosure;submission may be entered, for instance, as an expert input indicatinginterest in all persons sharing one or more characteristics with humansubject, such as all persons within a similar age range to humansubject, within an ethnic group matching the human subject, having thesame sex as the human subject, having similar symptoms, habits ofeating, exercise or the like to the human subject, labeled with the sameprognostic label or labels as human subject, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure willbe aware of various examples of ways to filter training data 120according to at least an expert submission and/or at least a diagnosticconstraint and/or expert submission. Machine-learning module 116 may beconfigured to generate the plurality of diagnostic outputs according tothe at least an expert submission by generating a plurality ofdiagnostic outputs and rank the plurality of diagnostic outputs usingthe at least an expert submission and/or diagnostic constraint.

In an embodiment, and continuing to refer to FIG. 1, wheremachine-learning module 116 generates a plurality of machine-learningmodels, machine-learning module 116 may be configured to generate the atleast a diagnostic output as a function of the at least an expertsubmission by selecting a machine-learning model as a function of the atleast a diagnostic constraint and/or the at least an expert submissionand generating the at least a diagnostic output using the selectedmachine-learning model. Selecting a machine-learning model as a functionof the at least diagnostic constraint may include selecting amachine-learning model that was generated according to a constraintmatching the at least a diagnostic constraint. For instance, trainingdata 120 may have been organized according to one or more diagnosticconstraints, and machine-learning module 116 may have previouslygenerated a plurality of machine-learning models using a plurality offiltered training datasets. As a non-limiting example, amachine-learning model may be generated for each of a list of commonconditions, for each of a list of common tests used to record biometricsamples, or any other category of medical data of interest to expertsand/or doctors; expert submissions collected as described above may listcategories and/or cohorts of data for which machine-learning modules 116are desirable, and machine-learning module 116 may generate models fromtraining data 120 organized according to such categories and/or cohorts.List of categories and/or cohorts may be provided via a graphical userinterface 108 to an expert providing the at least an expert submission;expert may enter at least an expert submission by selecting one suchcategory and/or cohort. Machine-learning module 116 may be configured togenerate the at least a diagnostic output as a function of the at leastan expert submission by combining at least a diagnostic output with theat least an expert submission; expert submission may be appended to,concatenated with, included with, and/or otherwise combined with atleast a diagnostic output.

With continued reference to FIG. 1, machine-learning module 116 mayinclude a prognostic label learner 148 operating on the classificationdevice 104, the prognostic label learner 148 designed and configured togenerate the at least a prognostic output as a function of the firsttraining set 124 and the at least a biological extraction. Prognosticlabel learner 148 may include any hardware and/or software module.Prognostic label learner 148 may be designed and configured to generateoutputs using machine learning processes as described above.

Still referring to FIG. 1, prognostic label learner 148 may be designedand configured to generate at least a prognostic output by creating atleast a first machine-learning model 152 relating physiological statedata to prognostic labels using the first training set 124 andgenerating the at least a prognostic output using the firstmachine-learning model 152; at least a first machine-learning model 152may include one or more models that determine a mathematicalrelationship between physiological state data and prognostic labels,and/or neural-net models, as described above.

Still referring to FIG. 1, prognostic label learner 148 may generateprognostic output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using first trainingset 124; the trained network may then be used to apply detectedrelationships between elements of physiological state data andprognostic labels.

Referring now to FIG. 7, machine-learning algorithms used by prognosticlabel learner 148 may include supervised machine-learning algorithms,which may, as a non-limiting example be executed using a supervisedlearning module 700 executing on classification device 104 and/or onanother computing device in communication with classification device104, which may include any hardware or software module. For instance, asupervised learning algorithm may use elements of physiological data asinputs, prognostic labels as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweenelements of physiological data and prognostic labels; scoring functionmay, for instance, seek to maximize the probability that a given elementof physiological state data 128 and/or combination of elements ofphysiological data is associated with a given prognostic label and/orcombination of prognostic labels to minimize the probability that agiven element of physiological state data 128 and/or combination ofelements of physiological state data is not associated with a givenprognostic label and/or combination of prognostic labels. Scoringfunction may be expressed as a risk function representing an “expectedloss” of an algorithm relating inputs to outputs, where loss is computedas an error function representing a degree to which a predictiongenerated by the relation is incorrect when compared to a giveninput-output pair provided in first training set 124. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various possible variations of supervised machine learning algorithmsthat may be used to determine relation between elements of physiologicaldata and prognostic labels. In an embodiment, one or more supervisedmachine-learning algorithms may be restricted to a particular domain forinstance, a supervised machine-learning process may be performed withrespect to a given set of parameters and/or categories of parametersthat have been suspected to be related to a given set of prognosticlabels, and/or are specified as linked to a medical specialty and/orfield of medicine covering a particular set of prognostic labels. As anon-limiting example, a particular set of blood test biomarkers and/orsensor data may be typically used by cardiologists to diagnose orpredict various cardiovascular conditions, and a supervisedmachine-learning process may be performed to relate those blood testbiomarkers and/or sensor data to the various cardiovascular conditions;in an embodiment, domain restrictions of supervised machine-learningprocedures may improve accuracy of resulting models by ignoringartifacts in training data 120. Domain restrictions may be suggested byexperts and/or deduced from known purposes for particular evaluationsand/or known tests used to evaluate prognostic labels. Additionalsupervised learning processes may be performed without domainrestrictions to detect, for instance, previously unknown and/orunsuspected relationships between physiological data and prognosticlabels.

Still referring to FIG. 7, machine-learning algorithms may includeunsupervised processes; unsupervised processes may, as a non-limitingexample, be executed by an unsupervised learning module 704 executing onclassification device 104 and/or on another computing device incommunication with classification device 104, which may include anyhardware or software module. For instance, and without limitation,prognostic label learner 148 and/or classification device 104 mayperform an unsupervised machine learning process on first training set124, which may cluster data of first training set 124 according todetected relationships between elements of the first training set 124,including without limitation correlations of elements of physiologicalstate data to each other and correlations of prognostic labels to eachother; such relations may then be combined with supervised machinelearning results to add new criteria for prognostic label learner 148 toapply in relating physiological state data to prognostic labels. As anon-limiting, illustrative example, an unsupervised process maydetermine that a first element of physiological data acquired in a bloodtest correlates closely with a second element of physiological data,where the first element has been linked via supervised learningprocesses to a given prognostic label, but the second has not; forinstance, the second element may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample a close correlation between first element of physiological statedata 128 and second element of physiological state data 128 may indicatethat the second element is also a good predictor for the prognosticlabel; second element may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstphysiological element by prognostic label learner 148.

With continued reference to FIG. 7, classification device 104 and/orprognostic label learner 148 may detect further significant categoriesof physiological data, relationships of such categories to prognosticlabels, and/or categories of prognostic labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added to system100, prognostic label learner 148 and/or classification device 104 maycontinuously or iteratively perform unsupervised machine-learningprocesses to detect relationships between different elements of theadded and/or overall data; in an embodiment, this may enable system 100to use detected relationships to discover new correlations between knownbiomarkers, prognostic labels, and/or ameliorative labels and one ormore elements of data in large bodies of data, such as genomic,proteomic, and/or microbiome-related data, enabling future supervisedlearning and/or lazy learning processes as described in further detailbelow to identify relationships between, e.g., particular clusters ofgenetic alleles and particular prognostic labels and/or suitableameliorative labels. Use of unsupervised learning may greatly enhancethe accuracy and detail with which system may detect prognostic labelsand/or ameliorative labels.

With continued reference to FIG. 7, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of ameliorative label; as illustrative examples, cohort couldinclude all people having a certain level or range of levels of bloodtriglycerides, all people diagnosed with type II diabetes, all peoplewho regularly run between 10 and 15 miles per week, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of a multiplicity of ways in which cohorts and/or other sets ofdata may be defined and/or limited for a particular unsupervisedlearning process.

Still referring to FIG. 7, prognostic label learner 148 mayalternatively or additionally be designed and configured to generate atleast a prognostic output by executing a lazy learning process as afunction of the first training set 124 and the at least a biologicalextraction; lazy learning processes may be performed by a lazy learningmodule 708 executing on classification device 104 and/or on anothercomputing device in communication with classification device 104, whichmay include any hardware or software module. A lazy-learning processand/or protocol, which may alternatively be referred to as a “lazyloading” or “call-when-needed” process and/or protocol, may be a processwhereby machine learning is conducted upon receipt of an input to beconverted to an output, by combining the input and training set toderive the algorithm to be used to produce the output on demand. Forinstance, an initial set of simulations may be performed to cover a“first guess” at a prognostic label associated with biologicalextraction, using first training set 124. As a non-limiting example, aninitial heuristic may include a ranking of prognostic labels accordingto relation to a test type of at least a biological extraction, one ormore categories of physiological data identified in test type of atleast a biological extraction, and/or one or more values detected in atleast a biological extraction; ranking may include, without limitation,ranking according to significance scores of associations betweenelements of physiological data and prognostic labels, for instance ascalculated as described above. Heuristic may include selecting somenumber of highest-ranking associations and/or prognostic labels.Prognostic label learner 148 may alternatively or additionally implementany suitable “lazy learning” algorithm, including without limitation aK-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or thelike; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various lazy-learning algorithms that maybe applied to generate prognostic outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

In an embodiment, and continuing to refer to FIG. 7, prognostic labellearner 148 may generate a plurality of prognostic labels havingdifferent implications for a particular person. For instance, where theat least a physiological sample includes a result of a dexterity test, alow score may be consistent with amyotrophic lateral sclerosis,Parkinson's disease, multiple sclerosis, and/or any number of less severdisorders or tendencies associated with lower levels of dexterity. Insuch a situation, prognostic label learner 148 and/or classificationdevice 104 may perform additional processes to resolve ambiguity.Processes may include filtering, sorting, and/or ranking plurality ofprognostic labels according to at least an expert submission and/ordiagnostic constraint as described above. Alternatively or additionally,processes may include additional machine learning steps; for instance,where reference to a model generated using supervised learning on alimited domain has produced multiple mutually exclusive results and/ormultiple results that are unlikely all to be correct, or multipledifferent supervised machine learning models in different domains mayhave identified mutually exclusive results and/or multiple results thatare unlikely all to be correct. In such a situation, prognostic labellearner 148 and/or classification device 104 may operate a furtheralgorithm to determine which of the multiple outputs is most likely tobe correct; algorithm may include use of an additional supervised and/orunsupervised model. Alternatively or additionally, prognostic labellearner 148 may perform one or more lazy learning processes using a morecomprehensive set of user data to identify a more probably correctresult of the multiple results. Results may be presented and/or retainedwith rankings, for instance to advise a medical professional of therelative probabilities of various prognostic labels being correct;alternatively or additionally, prognostic labels associated with aprobability of correctness below a given threshold and/or prognosticlabels contradicting results of the additional process, may beeliminated. As a non-limiting example, an endocrinal test may determinethat a given person has high levels of dopamine, indicating that a poorpegboard performance is almost certainly not being caused by Parkinson'sdisease, which may lead to Parkinson's being eliminated from a list ofprognostic labels associated with poor pegboard performance, for thatperson. Similarly, a genetic test may eliminate Huntington's disease, oranother disease definitively linked to a given genetic profile, as acause. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which additional processingmay be used to determine relative likelihoods of prognostic labels on alist of multiple prognostic labels, and/or to eliminate some labels fromsuch a list. Prognostic output 712 may be provided to client device asdescribed in further detail below.

Referring again to FIG. 1, machine-learning module 116 may include anameliorative process label learner 156 operating on the classificationdevice 104, the ameliorative process label learner 156 designed andconfigured to generate the at least an ameliorative output as a functionof the second training set 136 and the at least a prognostic output.Ameliorative process label learner 156 may include any hardware orsoftware module suitable for use as a prognostic label learner 148 asdescribed above. Ameliorative process label learner 156 is amachine-learning module 116 as described above; ameliorative processlabel learner 156 may perform any machine-learning process orcombination of processes suitable for use by a prognostic label learner148 as described above. For instance, and without limitation, andameliorative process label learner 156 may be configured to create asecond machine-learning model 160 relating prognostic labels toameliorative labels using the second training set 136 and generate theat least an ameliorative output using the second machine-learning model160; second machine-learning model 160 may be generated according to anyprocess, process steps, or combination of processes and/or process stepssuitable for creation of first machine learning model. In an embodiment,ameliorative process label learner 156 may use data from first trainingset 124 as well as data from second training set 136; for instance,ameliorative process label learner 156 may use lazy learning and/ormodel generation to determine relationships between elements ofphysiological data, in combination with or instead of prognostic labels,and ameliorative labels. Where ameliorative process label learner 156determines relationships between elements of physiological data andameliorative labels directly, this may determine relationships betweenprognostic labels and ameliorative labels as well owing to the existenceof relationships determined by prognostic label learner 148.

Referring now to FIG. 8, ameliorative process label learner 156 may beconfigured to perform one or more supervised learning processes, asdescribed above; supervised learning processes may be performed by asupervised learning module 800 executing on classification device 104and/or on another computing device in communication with classificationdevice 104, which may include any hardware or software module. Forinstance, a supervised learning algorithm may use prognostic labels asinputs, ameliorative labels as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweenprognostic labels and ameliorative labels; scoring function may, forinstance, seek to maximize the probability that a given prognostic labeland/or combination of prognostic labels is associated with a givenameliorative label and/or combination of ameliorative labels to minimizethe probability that a given prognostic label and/or combination ofprognostic labels is not associated with a given ameliorative labeland/or combination of ameliorative labels. In an embodiment, one or moresupervised machine-learning algorithms may be restricted to a particulardomain; for instance, a supervised machine-learning process may beperformed with respect to a given set of parameters and/or categories ofprognostic labels that have been suspected to be related to a given setof ameliorative labels, for instance because the ameliorative processescorresponding to the set of ameliorative labels are hypothesized orsuspected to have an ameliorative effect on conditions represented bythe prognostic labels, and/or are specified as linked to a medicalspecialty and/or field of medicine covering a particular set ofprognostic labels and/or ameliorative labels. As a non-limiting example,a particular set prognostic labels corresponding to a set ofcardiovascular conditions may be typically treated by cardiologists, anda supervised machine-learning process may be performed to relate thoseprognostic labels to ameliorative labels associated with varioustreatment options, medications, and/or lifestyle changes.

With continued reference to FIG. 8, ameliorative process label learner156 may perform one or more unsupervised machine-learning processes asdescribed above; unsupervised processes may be performed by anunsupervised learning module 804 executing on classification device 104and/or on another computing device in communication with classificationdevice 104, which may include any hardware or software module. Forinstance, and without limitation, ameliorative process label learner 156and/or classification device 104 may perform an unsupervised machinelearning process on second training set 136, which may cluster data ofsecond training set 136 according to detected relationships betweenelements of the second training set 136, including without limitationcorrelations of prognostic labels to each other and correlations ofameliorative labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria forameliorative process label learner 156 to apply in relating prognosticlabels to ameliorative labels. As a non-limiting, illustrative example,an unsupervised process may determine that a first prognostic label 132correlates closely with a second prognostic label 140, where the firstprognostic label 132 has been linked via supervised learning processesto a given ameliorative label, but the second has not; for instance, thesecond prognostic label 140 may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample, a close correlation between first prognostic label 132 andsecond prognostic label 140 may indicate that the second prognosticlabel 140 is also a good match for the ameliorative label; secondprognostic label 140 may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstprognostic label 132 by ameliorative process label learner 156.Unsupervised processes performed by ameliorative process label learner156 may be subjected to any domain limitations suitable for unsupervisedprocesses performed by prognostic label learner 148 as described above.

Still referring to FIG. 8, classification device 104 and/or ameliorativeprocess label learner 156 may detect further significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or categories of ameliorative labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added to system100, ameliorative process label learner 156 and/or classification device104 may continuously or iteratively perform unsupervisedmachine-learning processes to detect relationships between differentelements of the added and/or overall data; in an embodiment, this mayenable system 100 to use detected relationships to discover newcorrelations between known biomarkers, prognostic labels, and/orameliorative labels and one or more elements of data in large bodies ofdata, such as genomic, proteomic, and/or microbiome-related data,enabling future supervised learning and/or lazy learning processes toidentify relationships between, e.g., particular clusters of geneticalleles and particular prognostic labels and/or suitable ameliorativelabels. Use of unsupervised learning may greatly enhance the accuracyand detail with which system may detect prognostic labels and/orameliorative labels.

Continuing to view FIG. 8, ameliorative process label learner 156 may beconfigured to perform a lazy learning process as a function of thesecond training set 136 and the at least a prognostic output to producethe at least an ameliorative output; a lazy learning process may includeany lazy learning process as described above regarding prognostic labellearner 148. Lazy learning processes may be performed by a lazy learningmodule 808 executing on classification device 104 and/or on anothercomputing device in communication with classification device 104, whichmay include any hardware or software module. Ameliorative output 812 maybe provided to a client device 164 as described in further detail below.

In an embodiment, and still referring to FIG. 8, ameliorative processlabel learner 156 may generate a plurality of ameliorative processlabels 144 having different implications for a particular person. Forinstance, where a prognostic label indicates that a person has amagnesium deficiency, various dietary choices may be generated asameliorative labels associated with correcting the deficiency, such asameliorative labels associated with consumption of almonds, spinach,and/or dark chocolate, as well as ameliorative labels associated withconsumption of magnesium supplements. In such a situation, ameliorativeprocess label learner 156 and/or classification device 104 may performadditional processes to resolve ambiguity. Processes may includepresenting multiple possible results to a medical practitioner,informing the medical practitioner of various options that may beavailable, and/or that follow-up tests, procedures, or counseling may berequired to select an appropriate choice. Alternatively or additionally,processes may include additional machine learning steps. For instance,ameliorative process label learner 156 may perform one or more lazylearning processes using a more comprehensive set of user data toidentify a more probably correct result of the multiple results. Resultsmay be presented and/or retained with rankings, for instance to advise amedical professional of the relative probabilities of variousameliorative labels being correct or ideal choices for a given person;alternatively or additionally, ameliorative labels associated with aprobability of success or suitability below a given threshold and/orameliorative labels contradicting results of the additional process, maybe eliminated. As a non-limiting example, an additional process mayreveal that a person is allergic to tree nuts, and consumption ofalmonds may be eliminated as an ameliorative label to be presented.

Continuing to refer to FIG. 8, ameliorative process label learner 156may be designed and configured to generate further training data 120and/or to generate outputs using longitudinal data 816. As used herein,longitudinal data 816 may include a temporally ordered series of dataconcerning the same person, or the same cohort of persons; for instance,longitudinal data 816 may describe a series of blood samples taken oneday or one month apart over the course of a year. Longitudinal data 816may related to a series of samples tracking response of one or moreelements of physiological data recorded regarding a person undergoingone or more ameliorative processes linked to one or more ameliorativeprocess labels 144. Ameliorative process label learner 156 may track oneor more elements of physiological data and fit, for instance, a linear,polynomial, and/or splined function to data points; linear, polynomial,or other regression across larger sets of longitudinal data, using, forinstance, any regression process as described above, may be used todetermine a best-fit graph or function for the effect of a givenameliorative process over time on a physiological parameter. Functionsmay be compared to each other to rank ameliorative processes; forinstance, an ameliorative process associated with a steeper slope incurve representing improvement in a physiological data element, and/or ashallower slope in a curve representing a slower decline, may be rankedhigher than an ameliorative process associated with a less steep slopefor an improvement curve or a steeper slope for a curve marking adecline. Ameliorative processes associated with a curve and/or terminaldata point representing a value that does not associate with apreviously detected prognostic label may be ranked higher than one thatis not so associated. Information obtained by analysis of longitudinaldata 816 may be added to ameliorative process database and/or secondtraining set 136.

Referring again to FIG. 1, classification device 104 may be configuredto transmit an output including at least a diagnostic output to a clientdevice 164. A client device 164 may include, without limitation, adisplay in communication with classification device 104; display mayinclude any display as described below in reference to FIG. 10. A clientdevice 164 may include an addition computing device, such as a mobiledevice, laptop, desktop computer, or the like; as a non-limitingexample, the client device 164 may be a computer and/or workstationoperated by a medical professional. Output may be displayed on at leasta client device 164 using an output graphical user interface 108; outputgraphical user interface 108 may display one or more prognostic labelsof prognostic output and/or one or more ameliorative labels ofameliorative output. Alternatively or additionally, prognostic labelsand/or ameliorative labels may be translated into display data includingwithout limitation textual descriptions corresponding to prognosticlabels and/or ameliorative labels, one or more images associated withprognostic labels and/or ameliorative labels, and/or one or more videoor audio files associated with prognostic labels and/or ameliorativelabels; each of the above-described display data may be retrieved from adisplay data store, which may, for instance associate or link prognosticlabels, ameliorative labels, and/or elements of physiological data withone or more display data. Where output includes multiple prognosticlabels and/or multiple ameliorative labels, classification device 104may cause to a client device 164 to display the multiple labels and/ordisplay data associated therewith; labels may be displayed according torankings as described above, including without limitation rankings ofprognostic labels according to probability of correctness, ranking ofameliorative labels according to probability of efficacy, or the like.Significance scores, as calculated above, may be used to filter outputsas described in further detail below; for instance, where a number ofoutputs are generated and automated selection of a smaller number ofoutputs is desired, outputs corresponding to higher significance scoresmay be identified as more probable and/or selected for presentationwhile other outputs corresponding to lower significance scores may beeliminated.

With continued reference to FIG. 1, classification device 104 may beconfigured to display one or more follow-up suggestions at a clientdevice 164. One of more follow-up suggestions may include, withoutlimitation, suggestions for acquisition of an additional biologicalextraction, physiological input, and/or expert submission; in anembodiment, additional biological extraction may be provided toclassification device 104, which may trigger repetition of one or moreprocesses as described above, including without limitation generation ofprognostic output, refinement or elimination of ambiguous prognosticlabels of prognostic output, generation of ameliorative output, and/orrefinement or elimination of ambiguous ameliorative labels ofameliorative output. For instance, where a pegboard test result suggestspossible diagnoses of Parkinson's disease, Huntington's disease, ALS,and MS as described above, follow-up suggestions may include suggestionsto perform endocrinal tests, genetic tests, and/or electromyographictests; results of such tests may eliminate one or more of the possiblediagnoses, such that a subsequently displayed output only listsconditions that have not been eliminated by the follow-up test.Follow-up tests may include any receipt of any physiological sample asdescribed above.

With continued reference to FIG. 1, classification device 104 maydisplay one or more elements of contextual information, includingwithout limitation any patient medical history such as current labresults, a current reason for visiting a medical professional, currentstatus of one or more currently implemented treatment plans,biographical information concerning the patient, and the like. One ormore elements of contextual information may include goals a patientwishes to achieve with a medical visit or session, and/or as result ofinteraction with system 100. Contextual information may include one ormore questions a patient wishes to have answered in a medical visitand/or session, and/or as a result of interaction with system 100.Contextual information may include one or more questions to ask apatient. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various forms of contextual informationthat may be included, consistently with this disclosure. System 100 mayrecord a conversation between a patient and a medical professional forlater entry into medical records. Transmitting and/or generation of atleast a diagnostic output may include transmitting a machine learningmodel to client device; machine learning model may configure the clientdevice to output the at least a diagnostic output.

Embodiments of system 100 may furnish augmented intelligence systemsthat facilitate diagnostic, prognostic, curative, and/or therapeuticdecisions by medical professionals such as doctors. System 100 mayprovide fully automated tools and resources for each doctor to handle,process, diagnosis, develop treatment plans, facilitate and monitor allpatient implementation, and record each patient status. Provision ofexpert system elements via expert inputs and document-driven languageanalysis may ensure that recommendations generated by system 100 arebacked by the very best medical knowledge and practices in the world.Models and/or learners with access to data in depth may enablegeneration of recommendations that are directly personalized for eachpatient, providing complete confidence, mitigated risk, and completetransparency. Access to well-organized and personalized knowledge indepth may greatly enhance efficiency of medical visits; in embodiments,a comprehensive visit may be completed in as little as 10 minutes.Recommendations may further suggest follow up testing and/or therapy,ensuring an effective ongoing treatment and prognostic plan.

Referring now to FIG. 9, an exemplary embodiment of a method 900 ofclassification to prognostic labels using expert submissions isillustrated. At step 905, a classification device 104 records at least aphysiological input pertaining to a human subject; this may be performedas described above in reference to FIGS. 1-8. For instance, a person whois suffering from a physical complaint, such as knee pain, may enter atextual submission via a graphical user interface 108, by means ofelectronic communication, or the like describing the physical complaint;the person may alternatively or additionally describe the physicalcomplaint through any suitable means or method of communication to amedical professional or other expert, who may enter the descriptionaccording to any process described above. A persons who is sufferingfrom a psychological, emotional, or other constitutionally relevantcomplaint may similarly submit, enter, or provide information concerningthe complaint to a medical professional graphical user interface 108. Aperson may similarly enter questions regarding physical, emotional,psychological, nutritional, pharmaceutical, or other matters, questionsabout current and/or past treatment of the person or persons havingsimilar conditions, demographic factors, or the like, and/or adescription of current and/or past treatment, questions, and/orcomplaints. Each such entry provided from a person may be entered by anymeans or methods described above for entry of at least an expertsubmission, including entry using a graphical user interface 108, entryof a textual submission that is analyzed by a language processing module112, or the like. At least a physiological input may include anybiological extraction as described above; biological extraction may beretrieved from medical records, entered by an expert such as a medicalprofessional, entered and/or provided by human subject, and/or receivedfrom at least a sensor including without limitation a wearable device.As a non-limiting example, a person may approach an expert such as adoctor with a question or complaint, and the expert may order recordingof a biological extraction by having a test performed on user,retrieving medical records regarding the user, having the user fill outa questionnaire, or the like; this may be performed iteratively asdescribed in further detail below.

At step 910, and with continued reference to FIG. 9, classificationdevice 104 receives at least an expert submission pertaining to thehuman subject, the at least an expert submission including at least adiagnostic constraint; this may be performed as described above inreference to FIGS. 1-8. As a non-limiting example, where human subjectis a person who is visiting a doctor's office or otherwise consultingwith an expert, expert may eliminate certain categories and/ordescriptions of data describable by physiological state data, prognosticlabels, ameliorative process labels 144, or the like, may selectparticular topics of interest, getting more focused training data 120and/or models, may enter one or more demographic, age, or other facts reuser, past diagnoses, past treatment, or may otherwise limit or focusquestions to be answered or ranges of answers desired. In an embodiment,at least an expert submission includes a textual submission andreceiving the expert submission includes parsing the at least a textualsubmission and extract the at least a diagnostic constraint using alanguage processing module 112, for instance as described above inreference to FIGS. 1-8.

At step 915, and still referring to FIG. 9, classification device 104receives training data 120 relating physiological input data todiagnostic data; this may be implemented as described above in referenceto FIGS. 1-8. At step 920, classification device 104 generates at leasta diagnostic output using machine learning as a function of the at leastan expert submission and the at least a physiological input. This may beperformed as described above in reference to FIGS. 1-8. For instance,and without limitation, generating the at least a diagnostic output as afunction of the at least an expert submission may include filtering thetraining data 120 according to the at least a diagnostic constraint. Asa further non-limiting example, generating the at least a diagnosticoutput as a function of the at least an expert submission may includegenerating a plurality of machine-learning models, selecting amachine-learning model as a function of the at least a diagnosticconstraint, and generating the at least a diagnostic output using theselected machine-learning model, for instance as described above inreference to FIGS. 1-8. As a further example, generating the at least adiagnostic output as a function of the at least an expert submission mayinclude generating a plurality of diagnostic outputs and filtering theplurality of diagnostic outputs using the at least a diagnosticconstraint. As another non-limiting example, generating the at least adiagnostic output as a function of the at least an expert submission mayinclude generating a plurality of diagnostic outputs and ranking theplurality of diagnostic outputs using the at least an expert submission;this may be implemented as described above in reference to FIGS. 1-8.Generating the at least a diagnostic output as a function of the atleast an expert may include combining a machine-learning output with theat least an expert submission, for instance as described above inreference to FIGS. 1-8.

Still referring to FIG. 9, training data 120 may include a firsttraining set 124 including a plurality of first data entries, each firstdata entry of the first training set 124 including at least an elementof physiological state data 128 and at least a correlated firstprognostic label 132, for instance as described above in reference toFIGS. 1-8. Generating at least a diagnostic output as a function of theat least an expert submission generating at least a prognostic output asa function of the first training set 124 and the at least aphysiological test sample. Training data 120 may include a secondtraining set 136 including a plurality of second data entries, eachsecond data entry of the first training set 124 including at least asecond prognostic label 140 and at least a correlated ameliorativeprocess label 144, for instance as described above in reference to FIGS.1-8. Generating at least a diagnostic output as a function of the atleast an expert submission may include generating the at least anameliorative output as a function of the second training set 136 and atleast a prognostic label.

At step 925, classification device 104 transmits at least a diagnosticoutput to a client device 164; this may be implemented as describedabove in reference to FIGS. 1-8. For instance, an expert such as adoctor may receive output generated by above-described steps and/orsystem components, and may initiate one or more processes as describedabove a second or third time; new changed and/or updated physiologicalinput and/or expert submission may be provided, such as additionalinformation eliminating possible prognostic labels and/or ameliorativelabels, additional biological extraction data and/or data provided byuser, additional diagnostic constraints, or the like. A doctor may, forinstance, view an output suggesting two or more likely diseaseconditions and/or treatments, and eliminate one or the other based onprofessional judgement, personal and/or expert knowledge, and/orinformation provided by human subject concerning the latter's condition,history, and/or preferences. Similarly, a doctor or other expert mayorder follow-up tests based on personal and/or professional knowledge toobtain further constitutional and/or ameliorative information concerninghuman subject. As previously noted, any embodiment of method 900, of anyother method explicitly or implicitly disclosed in this disclosure, orany step or combination of steps thereof, may be performed iteratively.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 10 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1000 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1000 includes a processor 1004 and a memory1008 that communicate with each other, and with other components, via abus 1012. Bus 1012 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 1008 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1016 (BIOS), including basic routines thathelp to transfer information between elements within computer system1000, such as during start-up, may be stored in memory 1008. Memory 1008may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1020 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1008 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1000 may also include a storage device 1024. Examples ofa storage device (e.g., storage device 1024) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1024 may beconnected to bus 1012 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1024 (or one or more components thereof) may be removably interfacedwith computer system 1000 (e.g., via an external port connector (notshown)). Particularly, storage device 1024 and an associatedmachine-readable medium 1028 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1000. In one example,software 1020 may reside, completely or partially, withinmachine-readable medium 1028. In another example, software 1020 mayreside, completely or partially, within processor 1004.

Computer system 1000 may also include an input device 1032. In oneexample, a user of computer system 1000 may enter commands and/or otherinformation into computer system 1000 via input device 1032. Examples ofan input device 1032 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1032may be interfaced to bus 1012 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1012, and any combinations thereof. Input device 1032may include a touch screen interface that may be a part of or separatefrom display 10310, discussed further below. Input device 1032 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1000 via storage device 1024 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1040. A networkinterface device, such as network interface device 1040, may be utilizedfor connecting computer system 1000 to one or more of a variety ofnetworks, such as network 1044, and one or more remote devices 1048connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1044, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1020, etc.) may be communicated to and/or fromcomputer system 1000 via network interface device 1040.

Computer system 1000 may further include a video display adapter 1052for communicating a displayable image to a display device, such asdisplay device 1036. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1052 and display device 1036 maybe utilized in combination with processor 1004 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1000 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1012 via a peripheral interface 1056.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,and systems according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. A system for classification to prognostic labels using expert inputs,the system comprising: a classification device, the classificationdevice designed and configured to: record at least a physiological inputpertaining to a human subject; receive at least an expert submissionpertaining to the human subject, the at least an expert submissionincluding at least a diagnostic constraint; and transmit at least adiagnostic output to a client device; and a machine-learning moduleoperating on the classification device, the machine-learning moduledesigned and configured to: receive training data relating physiologicalinput data to diagnostic data; filter the training data according to theat least a diagnostic constraint; and generate at least a diagnosticoutput using machine learning as a function of the training data, the atleast an expert submission and the at least a physiological input, saiddiagnostic output identifying a treatment for the human subject.
 2. Thesystem of claim 1, wherein the at least a physiological input furthercomprises at least a biological extraction.
 3. The system of claim 1,wherein: the at least an expert submission further comprises a textualsubmission; and the classification device further comprises a languageprocessing module designed and configured to parse the at least atextual submission and extract the at least a diagnostic constraint. 4.(canceled)
 5. The system of claim 1, wherein: the machine learningmodule is configured to generate a plurality of machine-learning models;and the machine-learning module is configured generate the at least adiagnostic output by: selecting a machine-learning model as a functionof the at least a diagnostic constraint; and generating the at least adiagnostic output using the selected machine-learning model.
 6. Thesystem of claim 1, wherein the machine learning module is configured togenerate the at least a diagnostic output by: generating a plurality ofdiagnostic outputs; and filtering the plurality of diagnostic outputsusing the at least a diagnostic constraint.
 7. The system of claim 1,wherein the machine-learning module is further configured to generate aplurality of diagnostic outputs and rank the plurality of diagnosticoutputs using the at least an expert submission.
 8. The system of claim1, wherein the machine-learning module is further configured to combinea machine-learning output with the at least an expert submission.
 9. Thesystem of claim 1, wherein: the training data further comprises a firsttraining set including a plurality of first data entries, each firstdata entry of the first training set including at least an element ofphysiological state data and at least a correlated first prognosticlabel; and the machine-learning module further comprises a prognosticlabel learner operating on the classification device, the prognosticlabel learner designed and configured to generate at least a prognosticoutput as a function of the first training set and the at least aphysiological test sample.
 10. The system of claim 1, wherein: thetraining data further comprises a second training set including aplurality of second data entries, each second data entry of the firsttraining set including at least a second prognostic label and at least acorrelated ameliorative process label; and the machine-learning modulean ameliorative label learner operating on the classification device,the ameliorative label learner designed and configured to generate atleast an ameliorative output as a function of the second training setand at least a prognostic label.
 11. A method of classification toprognostic labels using expert inputs, the method comprising: recording,at a classification device, at least a physiological input pertaining toa human subject; receiving, at the classification device, at least anexpert submission pertaining to the human subject, the at least anexpert submission including at least a diagnostic constraint; receiving,at the classification device, training data relating physiological inputdata to diagnostic data; filtering the training data according to the atleast a diagnostic constraint; generating, by the classification device,at least a diagnostic output using machine learning as a function of theat least an expert submission and the at least a physiological input,said diagnostic output identifying a treatment for the human subject;and transmitting, by the classification device, at least a diagnosticoutput to a client device.
 12. The method of claim 11, wherein the atleast a physiological input further comprises at least a biologicalextraction.
 13. The method of claim 11, wherein: the at least an expertsubmission further comprises a textual submission; and receiving theexpert submission further comprises parsing the at least a textualsubmission and extract the at least a diagnostic constraint using alanguage processing module.
 14. (canceled)
 15. The method of claim 11,wherein generating the at least a diagnostic output as a function of theat least an expert submission further comprises: generating a pluralityof machine-learning models; selecting a machine-learning model as afunction of the at least a diagnostic constraint; and generating the atleast a diagnostic output using the selected machine-learning model. 16.The method of claim 11, wherein generating the at least a diagnosticoutput as a function of the at least an expert submission furthercomprises: generating a plurality of diagnostic outputs; and filteringthe plurality of diagnostic outputs using the at least a diagnosticconstraint.
 18. The method of claim 11, wherein generating the at leasta diagnostic output as a function of the at least an expert submissionfurther comprises generating a plurality of diagnostic outputs andranking the plurality of diagnostic outputs using the at least an expertsubmission.
 18. The method of claim 11, wherein generating the at leasta diagnostic output as a function of the at least an expert submissionfurther comprises combining a machine-learning output with the at leastan expert submission.
 19. The method of claim 11, wherein: the trainingdata further comprises a first training set including a plurality offirst data entries, each first data entry of the first training setincluding at least an element of physiological state data and at least acorrelated first prognostic label; and generating the at least adiagnostic output as a function of the at least an expert submissiongenerating at least a prognostic output as a function of the firsttraining set and the at least a physiological test sample.
 20. Themethod of claim 11, wherein: the training data further comprises asecond training set including a plurality of second data entries, eachsecond data entry of the first training set including at least a secondprognostic label and at least a correlated ameliorative process label;and generating the at least a diagnostic output as a function of the atleast an expert submission further comprises generating the at least anameliorative output as a function of the second training set and atleast a prognostic label.